Komputerowa analiza obrazów z endoskopu bezprzewodowego dla diagnostyki medycznej

• Autorzy: Szczypiński P. M.
• Języki publikacji: PL

Abstrakty

PL

Jednym z badań medycznych stosowanych w diagnostyce chorób przewodu pokarmowego jest bezprzewodowa endoskopia kapsułkowa. Wynikiem badania jest film, którego interpretacja przeprowadzana przez lekarza wymaga dużego skupienia uwagi, jest długotrwała i męcząca. Wyniki interpretacji nie są powtarzalne - zależą od wiedzy i doświadczenia konkretnego lekarza. Przedmiotem niniejszej monografii są opracowane przez autora metody numeryczne, których celem jest analiza obrazów cyfrowych z endoskopu bezprzewodowego zwiększające powtarzalność, wiarygodność oraz obiektywizm diagnozy medycznej. Mają one ograniczyć nakład pracy lekarza podczas oglądania filmu, zautomatyzować procedurę interpretacji oraz umożliwić analizę ilościową wybranych zmian chorobowych. Zaproponowane rozwiązania pozwalają między innymi na scharakteryzowanie ruchu własnego endoskopu względem przewodu pokarmowego, automatyczną regulację szybkości odtwarzania filmu, rekonstrukcję obrazu powierzchni przewodu pokarmowego, detekcję wybranych zmian chorobowych na podstawie cech barwy i tekstury obrazu oraz segmentację obrazów w celu wyodrębnienia obszarów objętych zmianami patologicznymi. Do najważniejszych osiągnięć opisanych w niniejszej monografii należą: model deformowalny do analizy ruchu własnego kamery, uniwersalna metoda obliczania naprężeń w modelach deformowalnych oraz nowy, szybki algorytm selekcji cech i klasyfikacji trudno rozdzielnych skupień wykorzystujący wielotop wypukły. Wszystkie opracowane metody poddano ocenie jakościowej i ilościowej, w szczególności zbadano ich przydatność do wspomagania interpretacji filmów endoskopowych. Efektem prowadzonych prac są udostępnione w interne-cie programy komputerowe, w których zastosowano opracowane algorytmy: WCE Player do analizy ruchu własnego endoskopu i rekonstrukcji powierzchni przewodu pokarmowego, a także MaZda do analizy cech barwy i tekstury obrazów oraz do klasyfikacji danych.

EN

Wireless capsule endoscopy is one of the medical tests used in diagnosis of gastrointestinal disorders. A result is a video of internal lumen of gastrointestinal tract which interpretation carried out by an expert gastroenterologist requires a lot of attention and is time consuming. The final diagnosis is rarely reproducible - it depends on the knowledge and experience of the diagnostic experience of the expert. The subject of this monograph is presentation and validation of novel algorithms for wireless endoscope video analysis whose purpose is to improve the reproducibility, reliability and objectivity of medical diagnosis. The algorithms are designed to reduce amount of work devoted to watching the movie, to automate the procedure of the data interpretation and to enable a quantitative description of selected lesions. The proposed methods allow to characterize the endoscope's egomotion (using a dedicated deformable model), reconstruct the intestine's internal surface, detect selected lesions (based on color and texture analysis), segment images in order to identify areas of pathological changes and dynamically adapt playback speed. The key achievements presented in this monograph include deformable model of rings for analysis of endoscopic camera egomotion passing through the gastrointestinal tract, versatile method for calculating tensions in the deformable model grid and an efficient algorithm using convex polytopes for feature selection and classification of specifically-shaped clusters. All the developed methods were implemented in a computer programs, and thereafter evaluated qualitatively and quantitatively. The computer programs - WCE Player for egomotion estimation and MaZda for image classification based on color and texture analysis - are available from the internet.

Słowa kluczowe

PL

obrazowanie diagnostyczne; endoskopia kapsułkowa; diagnostyka medyczna; analiza obrazu

EN

diagnostic imaging; wireless capsule endoscopy; medical diagnostics; image analysis

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Zeszyty Naukowe. Rozprawy Naukowe / Politechnika Łódzka
• Rocznik: 2012
• Tom: Z. 426
• Strony: 1--142
• Opis fizyczny: Bibliogr. 290 poz.

Twórcy

autor

Szczypiński P. M.

• Instytut Elektroniki Politechniki łódzkiej

Bibliografia

• [1] Fantastic Voyage – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Fantastic_Voyage. [Accessed: 11-Mar-2012].
• [2] Fantastyczna podróż / Fantastic Voyage (1966) – Filmweb. [Online]. Available: http://www.filmweb.pl/film/Fantastyczna+podróż-1966-33127. [Accessed: 11-Mar-2012].
• [3] G. Iddan, G. Meron, A. Glukhovsky and P. Swain, Wireless capsule endoscopy, Nature, vol. 405, no. 6785, p. 417, May 2000.
• [4] Invest in Israel – Given Imaging. [Online]. Available: http://www.investinisrael.gov.il/NR/exeres/01104877-8720-4F39-8441-BD24EC58D8B9.htm. [Accessed: 01-Oct-2011].
• [5] H. Yamamoto and H. Kita, Enteroscopy, J Gastroenterol, vol. 40, no. 6, pp. 555-562, Jul. 2005.
• [6] D. Koppel, Yuan-fang Wang and Hua Lee, Viewing enhancement in video-endoscopy, presented at the Applications of Computer Vision, 2002, pp. 304-308.
• [7] P. Swain, Role of video endoscopy in managing small bowel disease, Gut, vol. 53, no. 12, pp. 1866-1875, Dec. 2004.
• [8] M. Classen, G.N. Tytgat and C.J. Lightdale, Gastroenterological endoscopy. Thieme, 2002.
• [9] L.B. Gerson, Double-balloon enteroscopy: the new gold standard for small-bowel imaging?, Gastrointestinal Endoscopy, vol. 62, no. 1, pp. 71-75, Jul. 2005.
• [10] L.B. Gerson and J. Van Dam, Wireless Capsule Endoscopy and Double-Balloon Enteroscopy for the Diagnosis of Obscure Gastrointestinal Bleeding, Techniques in Vascular and Interventional Radiology, vol. 7, no. 3, pp. 130-135, Sep. 2004.
• [11] P.M. Szczypiński, R.D. Sriram, P.V.J. Sriram and D.N. Reddy, A model of deformable rings for interpretation of wireless capsule endoscopic videos, Medical Image Analysis, vol. 13, no. 2, pp. 312-324, Apr. 2009.
• [12] P.M. Szczypiński, P.V.J. Sriram, R.D. Sriram and D.N. Reddy, Computerized Image Analysis of Wireless Capsule Endoscopy Videos Using a Dedicated Weblike Model of Deformable Rings – A Feasibility Study, presented at the 12th United European Gastroenterology Week, Prague, 2004, vol. 36 (Suppl. I), p. A76.
• [13] P. Szczypiński, P.V.J. Sriram, R.D. Sriram and D.N. Reddy, Przetwarzanie i wspomaganie interpretacji danych z endoskopu bezprzewodowego za pomocą modelu deformowalnych pierścieni, Zeszyty Naukowe Elektronika, vol. 10, pp. 129-147, 2005.
• [14] P.M. Szczypinski, Selecting a motion estimation method for a Model of Deformable Rings, in ICSES’06 - International Conference on Signals and Electronic Systems, Proceedings, 2006, pp. 297-300.
• [15] P.M. Szczypiński, Przetwarzanie danych video z endoskopu bezprzewodowego za pomocą modelu deformowalnych pierścieni, presented at the Sympozjum TPO, Serock-Poland, 2006.
• [16] P.M. Szczypiński, P.V.J. Sriram, R.D. Sriram and D.N. Reddy, Model of Deformable Rings for Aiding the Wireless Capsule Endoscopy Video Interpretation and Reporting, in Computer Vision and Graphics, vol. 32, K. Wojciechowski, B. Smolka, H. Palus, R.S. Kozera, W. Skarbek and L. Noakes, Eds. Dordrecht: Kluwer Academic Publishers, 2006, pp. 167-172.
• [17] P.M. Szczypiński, Sterowanie odtwarzaniem wideo z endoskopu bezprzewodowego za pomocą współczynnika deformacji modelu deformowalnych pierścieni, presented at the XV Krajowa Konferencja Naukowa, Biocybernetyka i Inżynieria Biomedyczna, Wrocław, 2007.
• [18] P.M. Szczypiński, Computer Program for Aiding in Wireless Endoscopic Video Interpretation, in Computers in Medical Activity, vol. 65, E. Kącki, M. Rudnicki and J. Stempczyńska, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 53-61.
• [19] D. Terzopoulos, J. Platt, A. Barr and K. Fleischer, Elastically deformable models, ACM SIGGRAPH Computer Graphics, vol. 21, pp. 205-214, Aug. 1987.
• [20] T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: a survey, Medical Image Analysis, vol. 1, no. 2, pp. 91-108, Jun. 1996.
• [21] M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active contour models, International journal of computer vision, vol. 1, no. 4, pp. 321-331, 1988.
• [22] S. Osher and J. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol. 79, pp. 12-49, Nov. 1988.
• [23] P. Szczypiński and A. Materka, Variable-Flexibility Elastic Model for Digital Image Analysis, in XXI KKTOiUE, Kiekrz, 1998.
• [24] P. Szczypiński, Modele deformowalne do ilościowej analizy i rozpoznawania obiektów w obrazach cyfrowych, Rozprawa doktorska, Wydział Elektrotechniki i Elektroniki Politechniki Łódzkiej, Łódź, 2000.
• [25] Wielokomórka – Wikipedia, wolna encyklopedia. [Online]. Available: http://pl.wikipedia.org/wiki/Wielokomórka. [Accessed: 11-Mar-2012].
• [26] B. Block, G. Schachschal and H. Schmidt, Endoscopy of the upper GI tract: a training manual. Thieme, 2004.
• [27] J.D. Waye, D.K. Rex and C.B. Williams, Colonoscopy: principles and practice. Blackwell, 2003.
• [28] A. May, L. Nachbar, A. Wardak, H. Yamamoto and C. Ell, Double-balloon enteroscopy: preliminary experience in patients with obscure gastrointestinal bleeding or chronic abdominal pain, Endoscopy, vol. 35, no. 12, pp. 985-991, Dec. 2003.
• [29] K. Honda, T. Mizutani, K. Nakamura, N. Higuchi, K. Kanayama, Y. Sumida, S. Yoshinaga, S. Itaba, H. Akiho, K. Kawabe, Y. Arita and T. Ito, Acute pancreatitis associated with peroral double-balloon enteroscopy: a case report, World J. Gastroenterol., vol. 12, no. 11, pp. 1802-1804, Mar. 2006.
• [30] H.-H. Yen, Y.-Y. Chen, W.-W. Su, M.-S. Soon and Y.-M. Lin, Intestinal necrosis as a complication of epinephrine injection therapy during double-balloon enteroscopy, Endoscopy, vol. 38, no. 5, p. 542, May 2006.
• [31] D.G. Adler and C.J. Goustout, Wireless capsule endoscopy, Hosp Physician, vol. 39, no. 5, pp. 14-22, May 2003.
• [32] A. Bednarczuk and G. Rydzewska, Endoskopia kapsułkowa – nadal nowość czy już standard w diagnostyce gastroenterologicznej?, Polski Merkuriusz Lekarski, vol. XXVI, no. 155, pp. 506-511, 2009.
• [33] Given Imaging Ltd., Patients: Abdominal Pain, Crohn’s Disease, Gastrointestinal bleeding, Inflammatory Bowel Disease. [Online]. Available: http://www.givenimaging.com/en-us/Patients/Pages/CapsuleEndoscopy.aspx. [Accessed: 08-Sep-2011].
• [34] Olympus Corporation, EndoCapsule – Taking capsule endoscopy to the next level. [Online]. Available: http://www.olympus-europa.com/endoscopy/2001_5491.htm. [Accessed: 11-Mar-2012].
• [35] C. Gheorghe, R. Iacob and I. Bancila, Olympus capsule endoscopy for small bowel examination, Journal of Gastrointestinal and Liver Diseases, vol. 16, no. 3, p. 309, 2007.
• [36] A. Moglia, A. Menciassi, P. Dario and A. Cuschieri, Capsule endoscopy: progress update and challenges ahead, Nature Reviews Gastroenterology & Hepatology, vol. 6, pp. 353-361, May 2009.
• [37] Intromedic, MicroCam Info. [Online]. Available: http://www.intromedic.com/en/product/productInfo.asp. [Accessed: 11-Mar-2012].
• [38] C. Li, B. Zhang, C. Chen and Y. Li, OMOM capsule endoscopy in diagnosis of small bowel disease, Journal of Zhejiang University – Science B, vol. 9, no. 11, pp. 857-862, Nov. 2008.
• [39] C. Mc Caffrey, O. Chevalerias, C. O’Mathuna and K. Twomey, Swallowable-Capsule Technology, IEEE Pervasive Computing, vol. 7, pp. 23-29, Jan. 2008.
• [40] O. Ferrer-Roca, Telepathology and Optical Biopsy, International Journal of Telemedicine and Applications, vol. 2009, no. 740712, pp. 1-9, 2009.
• [41] RF Co. Ltd., The Next Generation Capsule Endoscope Sayaka. [Online]. Available: http://www.rfamerica.com/sayaka/index.html [Accessed: 11-Mar-2012].
• [42] Vector Consortium, Versatile Endoscopic Capsule for gastrointestinal TumOr Recognition and therapy. [Online]. Available: http://www.vector-project.com/. [Accessed: 11-Mar-2012].
• [43] P. Swain, The future of wireless capsule endoscopy, World Journal of Gastroenterology, vol. 14, p. 4142, 2008.
• [44] CORDIS, Nano based capsule-Endoscopy with molecular imaging and optical biopsy. [Online]. Available: http://cordis.europa.eu/search/index.cfm?fuseaction =proj.document&PJ_RCN=9776762 [Accessed: 08-Sep-2011].
• [45] Byungkyu Kim, Sukho Park, Chang Yeol Jee and Seok-Jin Yoon, An earthwormlike locomotive mechanism for capsule endoscopes, 2005, pp. 2997-3002.
• [46] Jiwoon Kwon, Sukho Park, Byungkyu Kim and Jong-Oh Park, Bio-Material Property Measurement System for Locomotive Mechanism in Gastro-Intestinal Tract, in Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, 2005, pp. 1303-1308.
• [47] Xiaona Wang, M. Q.-H. Meng and Yawen Chan, Physiological Factors of the Small Intestine in Design of Active Capsule Endoscopy, in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, 2005, pp. 2942-2945.
• [48] F. Carpi, S. Galbiati and A. Carpi, Controlled Navigation of Endoscopic Capsules: Concept and Preliminary Experimental Investigations, IEEE Transactions on Biomedical Engineering, vol. 54, pp. 2028-2036, Nov. 2007.
• [49] A. Chiba, M. Sendoh, K. Ishiyama and K.I. Arai, Magnetic actuator for capsule endoscope navigation system, presented at the INTERMAG, 2005, pp. 626-626.
• [50] N. Bourbakis, G. Giakos and A. Karargyris, Design of new-generation robotic capsules for therapeutic and diagnostic endoscopy, in Imaging Systems and Techniques (IST), 2010 IEEE International Conference on, 2010, pp. 1-6.
• [51] E. Scapa, H. Jacob, S. Lewkowicz, M. Migdal, D. Gat, A. Gluckhovski, N. Gutmann and Z. Fireman, Initial experience of wireless-capsule endoscopy for evaluating occult gastrointestinal bleeding and suspected small bowel pathology, Am. J. Gastroenterol, vol. 97, no. 11, pp. 2776–2779, Nov. 2002.
• [52] B. Li and M. Q.-H. Meng, Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images, Biomedical Engineering, IEEE Transactions on, vol. 56, no. 4, pp. 1032-1039, 2009.
• [53] B.S. Lewis and P. Swain, Capsule endoscopy in the evaluation of patients with suspected small intestinal bleeding: Results of a pilot study, Gastrointestinal Endoscopy, vol. 56, pp. 349-353, Sep. 2002.
• [54] “CAPVIEW.” [Online]. Available: http://www.capview.org/. [Accessed: 08-May-2012].
• [55] J.P.S. Cunha, M. Coimbra, P. Campos and J.M. Soares, Automated Topographic Segmentation and Transit Time Estimation in Endoscopic Capsule Exams, IEEE Transactions on Medical Imaging, vol. 27, pp. 19-27, 2008.
• [56] J.J. Christensen and D.L. Wingate, A Guide to gastrointestinal motility. Bristol John Wright Publisher, 1983.
• [57] Phooi Yee Lau and P.L. Correia, Detection of bleeding patterns in WCE video using multiple features, in Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 2007, pp. 5601-5604.
• [58] B. Giritharan, Xiaohui Yuan, Jianguo Liu, B. Buckles, JungHwan Oh and Shou Jiang Tang, Bleeding detection from capsule endoscopy videos, in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008, pp. 4780-4783.
• [59] J. Bonnel, A. Khademi, S. Krishnan and C. Ioana, Small bowel image classification using cross-co-occurrence matrices on wavelet domain, Biomedical Signal Processing and Control, vol. 4, no. 1, pp. 7-15, 2009.
• [60] B. Li and M.Q.-H. Meng, Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine, in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, 2009, pp. 498-503.
• [61] B. Li and M. Q.-H. Meng, Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments, Computers in Biology and Medicine, vol. 39, no. 2, pp. 141-147, 2009.
• [62] A. Karargyris and N. Bourbakis, A methodology for detecting blood-based abnormalities in Wireless Capsule Endoscopy videos, in BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on, 2008, pp. 1-6.
• [63] N. Bourbakis, Detecting Abnormal Patterns in WCE Images, in Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE’05), Minneapolis, MN, USA, pp. 232-238.
• [64] M.W. Mackiewicz, M. Fisher and C. Jamieson, Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification, in Proceedings of SPIE, San Diego, CA, USA, 2008, p. 69140R-69140R–12.
• [65] P. Szczypinski and A. Klepaczko, Selecting texture discriminative descriptors of capsule endoscopy images, in Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on, 2009, pp. 701-706.
• [66] P. Szczypiński and A. Klepaczko, Convex Hull-Based Feature Selection in Application to Classification of Wireless Capsule Endoscopic Images, in Advanced Concepts for Intelligent Vision Systems, vol. 5807, J. Blanc-Talon, W. Philips, D. Popescu and P. Scheunders, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 664-675.
• [67] A. Klepaczko and P.M. Szczypinski, Algorytm lokalnej aproksymacji wielomianowej w zastosowaniu do segmentacji obrazów endoskopowych, in XVI Krajowa Konferencja Biocybernetyka i Inżynieria Biomedyczna, Warszawa, 2010.
• [68] A. Klepaczko and P. Szczypiński, Automated Segmentation of Endoscopic Images Based on Local Shape-Adaptive Filtering and Color Descriptors, in Advanced Concepts for Intelligent Vision Systems, vol. 6474, J. Blanc-Talon, D. Bone, W. Philips, D. Popescu and P. Scheunders, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 245-254.
• [69] A. Klepaczko, P. Szczypiński, P. Daniel and M. Pazurek, Local Polynomial Approximation for Unsupervised Segmentation of Endoscopic Images, in Computer Vision and Graphics, vol. 6375, L. Bolc, R. Tadeusiewicz, L.J. Chmielewski and K. Wojciechowski, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 33-40.
• [70] P. Szczypiński and A. Klepaczko, Automated Recognition of Abnormal Structures in WCE Images Based on Texture Most Discriminative Descriptors, in Image Processing and Communications Challenges 2, vol. 84, R.S. Choraś, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 263-270.
• [71] P.M. Szczypiński and A. Klepaczko, Wykrywanie zmian patologicznych w filmach z endoskopu bezprzewodowego, presented at the VI Sympozjum Naukowe, Techniki Przetwarzania Obrazu, Serock, 2010.
• [72] P. Szczypiński, A. Klepaczko and M. Strzelecki, An Intelligent Automated Recognition System of Abnormal Structures in WCE Images, in Hybrid Artificial Intelligent Systems, vol. 6678, E. Corchado, M. Kurzyński and M. Woźniak, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 140-147.
• [73] M. Coimbra, P. Campos and J.P.S. Cunha, Topographic Segmentation and Transit Time Estimation for Endoscopic Capsule Exams, presented at the IEEE ICASSP, Toulouse-France, 2006, vol. 2, pp. 1164-1167.
• [74] M. Coimbra, J. Kustra, P. Campos and J.P.S. Cunha, Combining color with spatial and temporal position of the endoscopic capsule for improved topographic classification and segmentation, Proc. of SAMT, pp. 17-18, 2006.
• [75] M.T. Coimbra and J.P.S. Cunha, MPEG-7 Visual Descriptors – Contributions for Automated Feature Extraction in Capsule Endoscopy, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 16, no. 5, pp. 628-637, 2006.
• [76] A. Karargyris and N. Bourbakis, Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented, IEEE Engineering in Medicine and Biology Magazine, vol. 29, pp. 72-83, Jan. 2010.
• [77] M. Mackiewicz, J. Berens and M. Fisher, Wireless Capsule Endoscopy Color Video Segmentation, Medical Imaging, IEEE Transactions on, vol. 27, no. 12, pp. 1769-1781, 2008.
• [78] M.W. Mackiewicz, J. Berens and M. Fisher, Wireless capsule endoscopy video segmentation using support vector classifiers and hidden markov models, in Proc. International Conference Medical Image Understanding and Analyses, 2006, pp. 81-85.
• [79] M. Mackiewicz, J. Berens, M. Fisher and D. Bell, Colour and Texture Based Gastrointestinal Tissue Discrimination, in Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, 2006, vol. 2, p. II.
• [80] J. Berens, M.W. Mackiewicz and G.D. Bell, Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images, in Proceedings of SPIE, San Diego, CA, USA, 2005, pp. 283-290.
• [81] P. Spyridonos, F. Vilarino, J. Vitria, F. Azpiroz and P. Radeva, Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions, in Medical Image Computing and Computer-Assisted Intervention – MICCAI, vol. 4191, Springer Berlin / Heidelberg, 2006, pp. 161-168.
• [82] F. Vilarino, Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy, in Pattern Recognition, International Conference on, 2006, vol. 4, pp. 719-722.
• [83] F. Vilarino, L.I. Kuncheva and P. Radeva, ROC curves and video analysis optimization in intestinal capsule endoscopy, Pattern Recognition Letters, vol. 27, no. 8, pp. 875-881, Jun. 2006.
• [84] A. Karargyris and N. Bourbakis, A video-frame based registration using segmentation and graph connectivity for Wireless Capsule Endoscopy, in Life Science Systems and Applications Workshop, 2009. LiSSA 2009. IEEE/NIH, 2009, pp. 74-79.
• [85] M. Coimbra, P. Campos and J.P.S. Cunha, Extracting clinical information from endoscopic capsule exams using MPEG-7 visual descriptors, IEE Seminar Digests, vol. 2005, no. 11099, pp. 105-110, 2005.
• [86] W. Skarbek, MPEG-7, IX Konferencja PLOUG, Kościelisko, X, 2003.
• [87] T. Sikora, The MPEG-7 visual standard for content description-an overview, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 11, no. 6, pp. 696-702, 2001.
• [88] H. Eidenberger, How good are the visual MPEG-7 features, in SPIE & IEEE Visual Communications and Image Processing Conference, Lugano, Switzerland, 2003, pp. 476-488.
• [89] K. Mori, D. Deguchi, J. Hasegawa, Y. Suenaga, J. Toriwaki, H. Takabatake and H. Natori, A Method for Tracking the Camera Motion of Real Endoscope by Epipolar Geometry Analysis and Virtual Endoscopy System, in Medical Image Computing and Computer-Assisted Intervention, vol. 2208, W. Niessen and M. Viergever, Eds. Springer Berlin / Heidelberg, 2001, pp. 1-8.
• [90] K. Mori, D. Deguchi, J. Sugiyama, Y. Suenaga, J. Toriwaki, C.R. Maurer, H. Takabatake and H. Natori, Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images, Medical Image Analysis, vol. 6, no. 3, pp. 321-336, Sep. 2002.
• [91] J. Nagao, K. Mori, T. Enjouji, D. Deguchi, T. Kitasaka, Y. Suenaga, J.-I. Hasegawa, J.-I. Toriwaki, H. Takabatake and H. Natori, Fast and accurate bronchoscope tracking using image registration and motion prediction, in Lecture Notes in Computer Science, 2004, vol. 3217, pp. 551-558.
• [92] A. Karargyris, O. Karargyris and N. Bourbakis, 3D Representation of the Digestive Tract Surface in Wireless Capsule Endoscopy Videos, in BioInformatics and BioEngineering (BIBE), 2010 IEEE International Conference on, 2010, pp. 279-280.
• [93] A. Skalski, Segmentacja 3D danych medycznych pochodzących z tomografii komputerowej oraz endoskopowych zapisów wideo, Rozprawa Doktorska, Akademia Górniczo-Hutnicza, Wydział Elektrotechniki, Automatyki, Informatyki i Elektroniki, Kraków, 2009.
• [94] Choroba Leśniowskiego-Crohna – Wikipedia, wolna encyklopedia. [Online]. Available: http://pl.wikipedia.org/wiki/Choroba_Leśniowskiego-Crohna. [Accessed: 11-Mar-2012].
• [95] W. Latos, A. Gadowska-Cicha, K.J. Niepsuj and A. Sieroń, Choroba Leśniowskiego-Crohna w górnym odcinku przewodu pokarmowego, Wiad. Lek, vol. 58, pp. 222-226, 2005.
• [96] P. Szczypiński and A. Materka, Object Tracking and Recognition Using Deformable Grid with Geometrical Templates, in International Conference on Signals and Electronic Systems, Ustroń-Poland, 2000, pp. 169-174.
• [97] P. Szczypiński and A. Materka, Program do analizy obrazów za pomocą deformowalnych modeli, Zeszyty Naukowe Elektronika, vol. 5, pp. 33-51, 2000.
• [98] P. Szczypiński and A. Materka, Variable-Flexibility Elastic Model for Digital Image Analysis, Bulletin of the Polish Academy of Sciences, vol. 47, no. 3, pp. 263-269, 1999.
• [99] J. Jaksa, K. Ślot and P. Szczypiński, Face Recognition Using Deformable Models, in International Conference on Signals and Electronic Systems, Łódź-Poland, 2001, pp. 111-116.
• [100] P. Strumiłło and P. Szczypiński, Automatic Extraction of Fuzzy and Broken Image Edges using Active Contour Model, in XVIII KKTOiUE, 1995.
• [101] P. Strumiłło and P. Szczypiński, Application of an Active Contour Model for Extraction of Fuzzy and Broken Image Edges, Machine Graphics & Vision, vol. 5, no. 4, pp. 576-594, 1996.
• [102] P. Szczypiński, Center-Point Model of Deformable Surface, in Computer Vision and Graphics, vol. 32, K. Wojciechowski, B. Smolka, H. Palus, R.S. Kozera, W. Skarbek and L. Noakes, Eds. Dordrecht: Kluwer Academic Publishers, 2006, pp. 343-348.
• [103] P.M. Szczypinski, M. Strzelecki, A. Materka and A. Klepaczko, MaZda – A software package for image texture analysis, Computer Methods and Programs in Biomedicine, vol. 94, no. 1, pp. 6676, 2009.
• [104] P.M. Szczypiński, M. Strzelecki, A. Materka and A. Klepaczko, MaZda – The Software Package for Textural Analysis of Biomedical Images, in Computers in Medical Activity, vol. 65, E. Kącki, M. Rudnicki and J. Stempczyńska, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 73-84.
• [105] P.M. Szczypinski, M. Strzelecki and A. Materka, Mazda – a software for texture analysis, in 2007 International Symposium on Information Technology Convergence (ISITC 2007), Jeonju, Korea, 2007, pp. 245-249.
• [106] M. Strzelecki, A. Materka and P.M. Szczypinski, MaZda, in Texture analysis for magnetic resonance imaging, M. Hájek, Ed. Prague: Med4publishing, 2006.
• [107] M. Kociołek, P. Szczypiński, A. Materka and M. Strzelecki, Descrete Wavelet Transform-Derived Features for Digital Image Texture Analysis, in International Conference on Signals and Electronic Systems, Łódź-Poland, 2001, pp. 99-104.
• [108] M. Kociołek, A. Materka, M. Strzelecki and P. Szczypiński, Investigation of Wordlength Effect on Discriminative Power of Co-occurrence Matrix – Derived Features for Digital Image Texture Analysis, in International Conference on Signals and Electronic Systems, Ustroń-Poland, 2000, pp. 163-168.
• [109] M. Kociołek, A. Materka, M. Strzelecki and P. Szczypiński, Badanie wpływu liczby poziomów jasności obrazu na zdolność dyskryminacji tekstur przy użyciu macierzy zdarzeń, Zeszyty Naukowe Elektronika, vol. 5, pp. 21-33, 2000.
• [110] A.K. Thybo, P.M. Szczypinski, A.H. Karlsson, S. Donstrup, H.S. Stodkilde-Jorgensen and H.J. Andersen, Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods, Journal of Food Engineering, vol. 61, no. 1, pp. 91-100, Jan. 2004.
• [111] A.K. Thybo, A.H. Karlsson, H.C. Bertram, H.J. Andersen, P.M. Szczypinski and Donstrup, Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) in texture measurement, in Solid foods, D. Kilcast, Ed. Woodhead Publishing, 2004, pp. 184-204.
• [112] P. Szczypiński, M. Kociołek, A. Materka and M. Strzelecki, Computer Program for Image Texture Analysis in PhD Students Laboratory, presented at the International Conference on Signals and Electronic Systems, Łódź-Poland, 2001, pp. 255-262.
• [113] A. Amin and D. Mahmoud-Ghoneim, Texture analysis of liver fibrosis microscopic images: a study on the effect of biomarkers, Acta Biochimica et Biophysica Sinica, vol. 43, pp. 193-203, Jan. 2011.
• [114] S.A. Waugh, R.A. Lerski, L. Bidaut and A.M. Thompson, The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms, Medical Physics, vol. 38, p. 5058, 2011.
• [115] C. Vijayakumar and D. Gharpure, Development of image-processing software for automatic segmentation of brain tumors in MR images, Journal of Medical Physics, vol. 36, p. 147, 2011.
• [116] D. Mahmoud-Ghoneim, Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions, Theor Biol Med Model, vol. 8, p. 25, 2011.
• [117] M.S. de Oliveira, M.L.F. Balthazar, A D’Abreu, C.L. Yasuda, B.P. Damasceno, F. Cendes and G. Castellano, MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease, American Journal of Neuroradiology, vol. 32, pp. 60-66, 2011.
• [118] K.K. Holli, M. Waljas, L. Harrison, S. Liimatainen, T. Luukkaala, P. Ryymin, H. Eskola, S. Soimakallio, J. Ohman and P. Dastidar, Mild Traumatic Brain Injury: Tissue Texture Analysis Correlated to Neuropsychological and DTI Findings, Academic Radiology, vol. 17, no. 9, pp. 1096-1102, Sep. 2010.
• [119] M.E. Mayerhoefer, W. Schima, S. Trattnig, K. Pinker, V. Berger-Kulemann and A. Ba-Ssalamah, Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas, Journal of Magnetic Resonance Imaging, vol. 32, pp. 352-359, Jul. 2010.
• [120] L.C.V. Harrison, M. Raunio, K.K. Holli, T. Luukkaala, S. Savio, I. Elovaara, S. Soimakallio, H.J. Eskola and P. Dastidar, MRI Texture Analysis in Multiple Sclerosis: Toward a Clinical Analysis Protocol, Academic Radiology, vol. 17, no. 6, pp. 696-707, 2010.
• [121] A. Kassner and R.E. Thornhill, Texture Analysis: A Review of Neurologic MR Imaging Applications, American Journal of Neuroradiology, vol. 31, pp. 809-816, Apr. 2010.
• [122] K. Holli, A.-L. Laaperi, L. Harrison, T. Luukkaala, T. Toivonen, P. Ryymin, P. Dastidar, S. Soimakallio and H. Eskola, Characterization of Breast Cancer Types by Texture Analysis of Magnetic Resonance Images, Academic Radiology, vol. 17, no. 2, pp. 135-141, 2010.
• [123] M. Kozakiewicz, A. Marciniak-Hoffman and M. Olszycki, Comparative Analysis of Three Bone Substitute Materials Based on Co-Occurrence Matrix, Dent. Med. Probl, vol. 47, no. 1, pp. 23-29, 2010.
• [124] A. Amin and D. Mahmoud-Ghoneim, Zizyphus spina-christi protects against carbon tetrachloride-induced liver fibrosis in rats, Food and Chemical Toxicology, vol. 47, no. 8, pp. 2111-2119, Aug. 2009.
• [125] M.E. Mayerhoefer, P. Szomolanyi, D. Jirak, A. Berg, A. Materka, A. Dirisamer and S. Trattnig, Effects of Magnetic Resonance Image Interpolation on the Results of Texture-Based Pattern Classification, Investigative Radiology, vol. 44, pp. 405411, Jul. 2009.
• [126] M. Abella, J.M. Zubeldia, L. Conejero, N. Malpica, J.J. Vaquero and M. Desco, Automatic quantification of histological studies in allergic asthma, Cytometry Part A, vol. 75A, pp. 271-277, Mar. 2009.
• [127] S. Kumar and G.S. Mittal, Textural Characteristics of Five Microorganisms for Rapid Detection Using Image Processing, Journal of Food Process Engineering, vol. 32, pp. 126-143, Feb. 2009.
• [128] M. Kozakiewicz, A. Marciniak-Hoffman and M. Denkowski, Long Term Comparison of Application of Two Beta-Tricalcium Phosphates in Oral Surgery, Dent. Med. Probl, vol. 46, no. 4, pp. 384-388, 2009.
• [129] A. Kassner, F. Liu, R. E. Thornhill, G. Tomlinson and D. J. Mikulis, Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images, J. Magn. Reson. Imaging, vol. 30, no. 5, pp. 933-941, 2009.
• [130] L.C.V. Harrison, T. Luukkaala, H. Pertovaara, T.O. Saarinen, T.T. Heinonen, R. Järvenpää, S. Soimakallio, P.-L.I. Kellokumpu-Lehtinen, H.J. Eskola and P. Dastidar, Non-Hodgkin lymphoma response evaluation with MRI texture classification, J. Exp. Clin. Cancer Res., vol. 28, p. 87, 2009.
• [131] J. Zhang, L. Tong, L. Wang, and N.Li, Texture analysis of multiple sclerosis: a comparative study, Magnetic Resonance Imaging, vol. 26, no. 8, pp. 1160-1166, 2008.
• [132] P. Guggenbuhl, D. Chappard, M. Garreau, J.-Y. Bansard, G. Chales and Y. Rolland, Reproducibility of CT-based bone texture parameters of cancellous calf bone samples: Influence of slice thickness, European Journal of Radiology, vol. 67, no. 3, pp. 514-520, 2008.
• [133] L. Harrison, P. Dastidar, H. Eskola, R. Jarvenpaa, H. Pertovaara, T. Luukkaala, P.-L. Kellokumpu-Lehtinen and S. Soimakallio, Texture analysis on MRI images of non-Hodgkin lymphoma, Computers in Biology and Medicine, vol. 38, no. 4, pp. 519-524, Apr. 2008.
• [134] O. Yu, N. Parizel, L. Pain, B. Guignard, B. Eclancher, Y. Mauss and D. Grucker, Texture analysis of brain MRI evidences the amygdala activation by nociceptive stimuli under deep anesthesia in the propofol-formalin rat model, Magnetic Resonance Imaging, vol. 25, no. 1, pp. 144-146, Jan. 2007.
• [135] M. Kozakiewicz, M. Stefanczyk, A. Materka and P. Arkuszewski, Selected characteristic features of radiotexture of bone of the dental alveolus in the human maxilla and mandible, Magazyn Stomatologiczny, vol. 3, no. 181, pp. 40-43, 2007.
• [136] E. Lerouxel, H. Libouban, M.-F. Moreau, M.F. Basle, M. Audran and D. Chappard, Mandibular bone loss in an animal model of male osteoporosis (orchidectomized rat): a radiographic and densitometric study, Osteoporosis International, vol. 15, no. 10, pp. 814-819, Oct. 2004.
• [137] S. Herlidou, R. Grebe, F. Grados, N. Leuyer, P. Fardellone and M.-E. Meyer, Influence of age and osteoporosis on calcaneus trabecular bone structure: a preliminary in vivo MRI study by quantitative texture analysis, Magnetic Resonance Imaging, vol. 22, no. 2, pp. 237-243, Feb. 2004.
• [138] L. Bonilha, E. Kobayashi, G. Castellano, G. Coelho, E. Tinois, F. Cendes and L. M. Li, Texture Analysis of Hippocampal Sclerosis, Epilepsia, vol. 44, no. 12, pp. 1546-1550, 2003.
• [139] D. Jirák, M. Dezortová, P. Taimr and M. Hájek, Texture analysis of human liver, J. Magn. Reson. Imaging, vol. 15, no. 1, pp. 68-74, 2002.
• [140] J. Weng, T.S. Huang and N. Ahuja, 3-D Motion Estimation, Understanding and Prediction from Noisy Image Sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, pp. 370–389, May 1987.
• [141] W. Burger and B. Bhanu, Estimating 3D egomotion from perspective image sequence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 1040-1058, Nov. 1990.
• [142] T.Y. Tian, C. Tomasi and D.J. Heeger, Comparison of approaches to egomotion computation, presented at the Conference on Computer Vision and Pattern Recognition, 1996, pp. 315-320.
• [143] K. Daniilidis, Fixation Simplifies 3D Motion Estimation, Computer Vision and Image Understanding, vol. 68, no. 2, pp. 158-169, Nov. 1997.
• [144] T. Twardowski, T. Zielinski, K. Duda, M. Socha and M. Duplaga, Fast Estimation of Broncho-Fiberoscope Egomotion for Ct-Guided Transbronchial Biopsy, in Image Processing, 2006 IEEE International Conference on, 2006, pp. 1189-1192.
• [145] P.M. Szczypinski, R.D. Sriram, P.V.J. Sriram and D.N. Reddy, A model of deformable rings for interpretation of wireless capsule endoscopic videos, Medical Image Analysis, vol. 13, no. 2, pp. 312-324, Apr. 2009.
• [146] A. Verri and T. Poggio, Motion field and optical flow: qualitative properties, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 11, no. 5, pp. 490-498, 1989.
• [147] B.K.P. Horn and B.G. Schunck, Determining optical flow, Artificial Intelligence, vol. 17, no. 1-3, pp. 185-203, 1981.
• [148] B. Glocker, N. Komodakis, G. Tziritas, N. Navab and N. Paragios, Dense image registration through MRFs and efficient linear programming, Medical Image Analysis, vol. 12, no. 6, pp. 731741, Dec. 2008.
• [149] S.S. Beauchemin and J.L. Barron, The computation of optical flow, ACM Comput. Surv., vol. 27, no. 3, pp. 433-466, 1995.
• [150] Motion Analysis and Object Tracking – opencv v2.1 documentation. [Online]. Available: http://opencv.willowgarage.com/documentation/cpp/video_motion_analysis_and_ object_tracking.html?highlight=optical flow. [Accessed: 02-Mar-2012].
• [151] B. Zitova and J. Flusser, Image registration methods: a survey, Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, 2003.
• [152] J.B.A. Maintz and M.A. Viergever, A survey of medical image registration, Medical Image Analysis, vol. 2, no. 1, pp. 1-36, Mar. 1998.
• [153] M. Holden, A Review of Geometric Transformations for Nonrigid Body Registration, IEEE Transactions on Medical Imaging, vol. 27, pp. 111-128, Jan. 2008.
• [154] B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, in Proc. Int"l Joint Conf. Artificial Intelligence, 1981, pp. 674-679.
• [155] W.M. Neuenschwander, P. Fua, L. Iverson, G. Székely and O. Kübler, Ziplock Snakes, International Journal of Computer Vision, vol. 25, no. 3, pp. 191-201, Dec. 1997.
• [156] L.D. Cohen and I. Cohen, Finite-element methods for active contour models and balloons for 2-D and 3-D images, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 15, no. 11, pp. 1131-1147, 1993.
• [157] C. Chesnaud, P. Refregier and V. Boulet, Statistical region snake-based segmentation adapted to different physical noise models, IEEE Trans. Pattern Anal. Machine Intell., vol. 21, no. 11, pp. 1145-1157, Nov. 1999.
• [158] S.R. Gunn and M.S. Nixon, A robust snake implementation; a dual active contour, IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 1, pp. 63-68, Jan. 1997.
• [159] F. Leymarie and M.D. Levine, Simulating the grassfire transform using an active contour model, IEEE Trans. Pattern Anal. Machine Intell., vol. 14, no. 1, pp. 56-75, Jan. 1992.
• [160] S. Lobregt and M.A. Viergever, A discrete dynamic contour model, IEEE Transactions on Medical Imaging, vol. 14, pp. 12-24, Mar. 1995.
• [161] S. Menet, P. Saint-Marc and G. Medioni, Active contour models: overview, implementation and applications, in IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Los Angeles, CA, USA, 1990, pp. 194-199.
• [162] T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: a survey, Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
• [163] L.H. Staib and J.S. Duncan, Parametrically deformable contour models, presented at the Computer Vision and Pattern Recognition, 1989, pp. 98-103.
• [164] L.H. Staib and J.S. Duncan, Boundary finding with parametrically deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 1061-1075, Nov. 1992.
• [165] W. Neuenschwander, P. Fua, G. Székely, O. Kuebler, A. Gruen and O. Kuebler, From ziplock snakes to velcro surfaces, in Automatic extraction of man-made objects from aerial and space images, Birkhauser Verlag Basel, 1995.
• [166] Y.F. Wang and J.-F. Wang, Surface reconstruction using deformable models with interior and boundary constraints, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 572-579, May 1992.
• [167] W. Neuenschwander, P. Fua, G. Szekely and O. Kubler, Deformable Velcro surfaces, presented at the Computer Vision, 1995, pp. 828-833.
• [168] P. Makowski, T.S. S?rensen, S.V. Therkildsen, A. Materka, H. St?dkilde-J?rgensen and E.M. Pedersen, Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization, Computerized Medical Imaging and Graphics, vol. 26, no. 1, pp. 9 -17, 2002.
• [169] L.D. Cohen and I. Cohen, Finite-element methods for active contour models and balloons for 2-D and 3-D images, IEEE Trans. Pattern Anal. Machine Intell., vol. 15, no. 11, pp. 1131-1147, Nov. 1993.
• [170] L.D. Cohen, E. Bardinet and N. Ayache, Surface reconstruction using active contour models, Jan. 1992.
• [171] L.D. Cohen, On active contour models and balloons, CVGIP: Image understanding, vol. 53, no. 2, pp. 211-218, 1991.
• [172] A.A. Amini, P. Shi, R.T. Constable, K. Johnson, J.S. Duncan and J.C. Gore, Energy-minimizing deformable grids for tracking tagged MR cardiac images, presented at the Computers in Cardiology, 1992, pp. 651-654.
• [173] Cheng-Yuan Liou and Quan-Ming Chang, Meshed snakes, presented at the Neural Networks, 1996, vol. 3, pp. 1516-1521.
• [174] H. Delingette, Adaptive and deformable models based on simplex meshes, in Motion of Non-Rigid and Articulated Objects, 1994., Proceedings of the 1994 IEEE Workshop on, 1994, pp. 152-157.
• [175] G.D. Hager and P.N. Belhumeur, Efficient region tracking with parametric models of geometry and illumination, IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 10, pp. 1025-1039, Oct. 1998.
• [176] A.K. Jain, Y. Zhong and M.-P. Dubuisson-Jolly, Deformable template models: A review, Signal Processing, vol. 71, pp. 109-129, Dec. 1998.
• [177] P. van Beek, A. M. Tekalp, N. Zhuang, I. Celasun, and Minghui Xia, Hierarchical 2-D mesh representation, tracking, and compression for object-based video, IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 2, pp. 353-369, Mar. 1999.
• [178] Yao Wang and O. Lee, Use of two-dimensional deformable mesh structures for video coding .I. The synthesis problem: mesh-based function approximation and mapping, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 6, no. 6, pp. 636-646, 1996.
• [179] Yao Wang, O. Lee and A. Vetro, Use of two-dimensional deformable mesh structures for video coding. II. The analysis problem and a region-based coder employing an active mesh representation, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 6, no. 6, pp. 647-659, 1996.
• [180] Yao Wang and Ouseb Lee, Active mesh-a feature seeking and tracking image sequence representation scheme, IEEE Transactions on Image Processing, vol. 3, pp. 610-624, Sep. 1994.
• [181] J.A. Sethian, Fast marching methods, SIAM review, pp. 199-235, 1999.
• [182] J. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, 2nd ed., reprint. Cambridge Univ. Press, 2002.
• [183] S. Osher, Geometric level set methods in imaging, vision, and graphics. New York: Springer, 2003.
• [184] R. Malladi, Geometric methods in bio-medical image processing. Berlin – New York: Springer, 2002.
• [185] Xiao Han, Chenyang Xu and J.L. Prince, Topology preserving level set method for geometric deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 755-768, Jun. 2003.
• [186] J. Chen and A.A. Amini, Quantifying 3-D Vascular Structures in MRA Images Using Hybrid PDE and Geometric Deformable Models, IEEE Transactions on Medical Imaging, vol. 23, pp. 1251-1262, Oct. 2004.
• [187] Chenyang Xu, A. Yezzi and J.L. Prince, On the relationship between parametric and geometric active contours, presented at the Conference on Signals, Systems and Computers, 2000, vol. 1, pp. 483-489.
• [188] Haiyan Wang and Bijoy Ghosh, Geometric active deformable models in shape modeling, IEEE Transactions on Image Processing, vol. 9, pp. 302-308, Feb. 2000.
• [189] C.P. Lee, Robust Image Segmentation using Active Contours: Level Set Approaches. North Carolina State University, Department of Electrical and Computer Engineering, 2005.
• [190] R. Malladi, J.A. Sethian and B.C. Vemuri, Shape modeling with front propagation: a level set approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, pp. 158-175, Feb. 1995.
• [191] W.J. Niessen, B.M.T.H. Romeny and M.A. Viergever, Geodesic deformable models for medical image analysis, IEEE Transactions on Medical Imaging, vol. 17, pp. 634-641, Aug. 1998.
• [192] T.F. Chan and L.A. Vese, Active contours without edges, Image Processing, IEEE Transactions on, vol. 10, no. 2, pp. 266-277, 2001.
• [193] M.E. Alexander and R.L. Somorjai, The registration of MR images using multiscale robust methods, Magnetic Resonance Imaging, vol. 14, no. 5, pp. 453-468, 1996.
• [194] J. Feldmar and N. Ayache, Rigid, affine and locally affine registration of free-form surfaces, International Journal of Computer Vision, vol. 18, no. 2, pp. 99-119, May 1996.
• [195] W.R. Crum, T. Hartkens and D. Hill, Non-rigid image registration: theory and practice, British journal of radiology, vol. 77, no. Special Issue 2, p. S140, 2004.
• [196] R. Baggs and D. Tamir, Non-Rigid Image Registration, in Proceedings of the Twenty-First International FLAIRS Conference, 2008.
• [197] J.P.W. Pluim, J.B.A. Maintz and M.A. Viergever, Mutual-information-based registration of medical images: A survey, IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 986-1004, 2003.
• [198] P. A. van den Elsen, E.-J. D. Pol and M.A. Viergever, “Medical image matching-a review with classification,” IEEE Engineering in Medicine and Biology Magazine, vol. 12, pp. 26-39, Mar. 1993.
• [199] K. Rohr, Elastic registration of multimodal medical images: A survey, Kunstliche Intelligenz, vol. 14, no. 3, pp. 11-17, 2000.
• [200] H. Chui and A. Rangarajan, A new point matching algorithm for non-rigid registration, Computer Vision and Image Understanding, vol. 89, no. 2-3, pp. 114-141, 2003.
• [201] J. Santamaria, O. Cordon and S. Damas, A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling, Computer Vision and Image Understanding, vol. 115, no. 9, pp. 1340-1354, 2011.
• [202] J. Shi and C. Tomasi, Good features to track, presented at the Computer Vision and Pattern Recognition, Seattle, WA , USA, 1994.
• [203] Pohsiang Hsu, K.J.R. Liu and T. Chen, A low bit-rate video codec based on two-dimensional mesh motion compensation with adaptive interpolation, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 11, no. 1, pp. 111-117, 2001.
• [204] W. Badawy and M. Bayoumi, A scalable affine core for video object motion compensation, in Computer Architectures for Machine Perception, 2000. Proceedings. Fifth IEEE International Workshop on, 2000, pp. 142-146.
• [205] J.Y. Tham, S. Ranganath, M. Ranganath and A.A. Kassim, A novel unrestricted center-biased diamond search algorithm for block motion estimation, Circuits nd Systems for Video Technology, IEEE Transactions on, vol. 8, no. 4, pp. 369-377, 1998.
• [206] J. Jain and A. Jain, Displacement measurement and its application in interframe image coding, Communications, IEEE Transactions on, vol. 29, no. 12, pp. 1799-1808, 1981.
• [207] A.M. Tourapis, Enhanced predictive zonal search for single and multiple frame motion estimation, in Proceedings of Visual Communications and Image Processing, 2002, vol. 1998, pp. 1069-1079.
• [208] A.M. Tourapis, O.C. Au, M.L. Liou and others, Predictive motion vector field adaptive search technique (PMVFAST)-enhancing block based motion estimation, in Proc. SPIE Conf. Visual Communication and Image Processing, 2001, pp. 883-892.
• [209] J. Watkinson, The MPEG handbook: MPEG-1, MPEG-2, MPEG-4. Elsevier/Focal Press, 2004.
• [210] R.O. Duda, P.E. Hart and D.G. Stork, Pattern classification. 2nd ed. Chichester: Wiley-Interscience, 2001.
• [211] R.C. Gonzalez and R.E. Woods, Digital image processing. Prentice-Hall, Inc., Upper Saddle River, NJ, 2006.
• [212] Cross-correlation – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation. [Accessed: 02-Mar-2012].
• [213] Mutual information – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Mutual_information. [Accessed: 02-Mar-2012].
• [214] Mean squared error – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Mean_squared_error. [Accessed: 02-Mar-2012].
• [215] Mean difference – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Mean_absolute_difference. [Accessed: 02-Mar-2012].
• [216] W. Neuenschwander, P. Fua, G. Szekely and O. Kuebler, Velcro Surfaces: Fast Initialization of Deformable Models, Computer Vision and Image Understanding, vol. 65, pp. 237-245, Feb. 1997.
• [217] I.N. Bankman, Handbook of medical imaging: processing and analysis. San Diego CA: Academic Press, 2000.
• [218] C. Xu, J.L. Prince and A.Y. Jr, A Summary of Geometric Level-Set Analogues for a General Class of Parametric Active Contour and Surface Models, in Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM’01), 2001, p. 104.
• [219] T. Pavlidis, Grafika i przetwarzanie obrazów. Warszawa: WNT, 1987.
• [220] M. Pesce, Programming Microsoft DirectShow for digital video and television. Redmond Wash.: Microsoft Press, 2003.
• [221] DirectShow – Microsoft Developer Network, Microsoft Developer Network. [Online]. Available: http://msdn.microsoft.com/en-us/library/ms783323(VS.85).aspx. [Accessed: 02-Mar-2012].
• [222] P. Szczypiński, P. Pełczyński, D. Szajerman and P. Strumiłło, Implementation of Computer Vision Algorithms in DirectShow Technology, in Image Processing and Communications Challenges 2, vol. 84, R.S. Choraś, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 31-38.
• [223] POV-Ray – The Persistence of Vision Raytracer. [Online]. Available: http://www.povray.org/. [Accessed: 03-Oct-2011].
• [224] K. Fukunaga, Introduction to statistical pattern recognition. Academic Press, 1990.
• [225] Linear discriminant analysis – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Linear_discriminant_analysis. [Accessed: 02-Mar-2012].
• [226] R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems,”Annals of Human Genetics, vol. 7, pp. 179-188, Sep. 1936.
• [227] J. Cohen, Applied multiple regression/correlation analysis for the behavioral sciences. L. Erlbaum Associates, 2003.
• [228] D. Hand, Principles of data mining. Cambridge Mass.: MIT Press, 2001.
• [229] Linear regression – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Linear_regression. [Accessed: 02-Mar-2012].
• [230] A. Materka and M. Strzelecki, Texture analysis methods-a review, Technical University of Lodz, Institute of Electronics, COST B11, 1998.
• [231] A.K. Jain, Fundamentals of digital image processing. Prentice-Hall, Inc., 1989.
• [232] M. Tuceryan, Texture Analysis, in Handbook of pattern recognition & computer vision, C. Chen, Ed. Singapore – River Edge NJ: World Scientific, 1993.
• [233] A.C. Bovik, M. Clark and W.S. Geisler, Multichannel texture analysis using localized spatial filters, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, no. 1, pp. 55-73, 1990.
• [234] R.M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol. 67, no. 5, pp. 786-804, 1979.
• [235] R.M. Haralick, K. Shanmugam and I. Dinstein, Textural Features for Image Classification, Systems, Man and Cybernetics, IEEE Transactions on, vol. 3, no. 6, pp. 610-621, 1973.
• [236] L. Cohen, Time-frequency distributions-a review, Proceedings of the IEEE, vol. 77, no. 7, pp. 941-981, 1989.
• [237] J.G. Daugman, Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression, Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 36, no. 7, pp. 1169-1179, 1988.
• [238] D. Mahmoud-Ghoneim, A. Amin and P. Corr, MRI-based texture analysis: a potential technique to assess protectors against induced-liver fibrosis in rats, Radiology and Oncology, vol. 43, pp. 30-40, Mar. 2009.
• [239] M. Kociński, A. Klepaczko, A. Materka, M. Chekenya and A. Lundervold, 3D image texture analysis of simulated and real-world vascular trees, Computer Methods and Programs in Biomedicine, vol. In Press, Corrected Proof, 2011.
• [240] P. Zapotoczny, M. Zielińska and Z. Nita, Application of image analysis for the varietal classification of barley: Morphological features, Journal of Cereal Science, vol. 48, no. 1, pp. 104-110, Jul. 2008.
• [241] M. Abella, J. M. Zubeldia, L. Conejero, N. Malpica, J. J. Vaquero and M. Desco, Automatic quantification of histological studies in allergic asthma, Cytometry Part A, vol. 75, no. 3, pp. 271-277, 2009.
• [242] K. Holli, A.-L. Lääperi, L. Harrison, S. Soimakallio, P. Dastidar and H.J. Eskola, Detection of characteristic texture parameters in breast MRI, in 4th European Conference of the International Federation for Medical and Biological Engineering, vol. 22, J. Sloten, P. Verdonck, M. Nyssen and J. Haueisen, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 517-521.
• [243] G. Chen, S. Jespersen, M. Pedersen, Q. Pang, M.R. Horsman and H.S. Jorgensen, Evaluation of anti-vascular therapy with texture analysis, Anticancer research, vol. 25, no. 5, p. 3399, 2005.
• [244] L.C.V. Harrison, P. Dastidar, K.K. Holli, S. Savio, A. Autere, A. Oinonen, V. Pylkki, S. Soimakallio and H. Eskola, Manual Segmentation of Brain Tissue and Multiple Sclerosis Lesions for Texture Analysis, in World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany, vol. 25/2, O. Dössel and W. C. Schlegel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 300-303.
• [245] V. Punys, J. Puniene, R. Jurkevicius, and J. Punys, Myocardium tissue analysis based on textures in ultrasound images, Studies in Health Technology and Informatics, vol. 116, pp. 435-440, 2005.
• [246] K.K. Holli, L. Harrison, P. Dastidar, M. Wäljas, J. Öhman, S. Soimakallio and H. Eskola, Texture Analysis of Corpus Callosum in Mild Traumatic Brain Injury Patients, in World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany, vol. 25/4, O. Dössel and W.C. Schlegel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 37-40.
• [247] D. Mahmoud-Ghoneim, Y. Cherel, L. Lemaire, J.D. de Certaines and A. Maniere, Texture analysis of magnetic resonance images of rat muscles during atrophy and regeneration, Magnetic Resonance Imaging, vol. 24, no. 2, pp. 167-171, Feb. 2006.
• [248] R. Zhou and Y. Li, Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network, Magnetic Resonance Imaging, vol. 25, no. 5, pp. 727-732, 2007.
• [249] J.J. Szymanski, J.T. Jamison and D.J. DeGracia, Texture analysis of poly-adenylated mRNA staining following global brain ischemia and reperfusion, Computer Methods and Programs in Biomedicine, vol. In Press, Corrected Proof, 2001.
• [250] A. Materka, P. Cichy and J. Tuliszkiewicz, Texture analysis of x-ray images for detection of changes in bone mass and structure, Texture Analysis in Machine Vision, Series in Machine Perception & Machine Intelligence, vol. 40, pp. 189-195, 2000.
• [251] J.M. Martínez, MPEG-7 Overview. ISO/IEC, Oct-2004.
• [252] P. Owczarek, T. Rosiński, Model eksperymentalny opisu treści wizyjnych, in Instytut Elektroniki i Telekomunikacji, Politechnika Poznańska, Poznań, 2003.
• [253] Y. Zhong and A.K. Jain, Object localization using color, texture and shape, Pattern Recognition, vol. 33, no. 4, pp. 671-684, Apr. 2000.
• [254] Co-occurrence matrix – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Co-occurrence_matrix. [Accessed: 05-Oct-2011].
• [255] S.G. Mallat, Multifrequency channel decompositions of images and wavelet models, Acoustics, Speech and Signal Processing, IEEE Transactions on DOI – 10.1109/29.45554, vol. 37, no. 12, pp. 2091-2110, 1989.
• [256] S.G. Mallat, Review of multifrequency channel decompositions of images and wavelet models. New York University, Courant Institute of Mathematical Sciences, Computer Science Division, 1988.
• [257] B. Li and M.Q.-H. Meng, Ulcer recognition in capsule endoscopy images by texture features, in Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, 2008, pp. 234-239.
• [258] S.J. Sangwine and R.E. Horne, The colour image processing handbook. Chapman & Hall, 1998.
• [259] R.W. Hunt, The reproduction of colour. John Wiley & Sons, 2004.
• [260] A. Materka and P. Strumiłło, Wstęp do komputerowej analizy obrazów - Skrypt. Łódź: Politechnika Łódzka, 2009.
• [261] List of color spaces and their uses - Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/List_of_color_spaces_and_their_uses. [Accessed: 02-Mar-2012].
• [262] K. Fukunaga, Introduction to statistical pattern recognition. Academic Press, 1990.
• [263] V. Vapnik, The nature of statistical learning theory, 2nd ed. New York: Springer, 2000.
• [264] A.K. Jain, R.P.W. Duin and Jianchang Mao, Statistical pattern recognition: a review, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 1, pp. 4-37, 2000.
• [265] CJ.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, Jun. 1998.
• [266] P. Yee, Regularized radial basis function networks: theory and applications. New York: John Wiley, 2001.
• [267] J. Moody and C.J. Darken, Fast Learning in Networks of Locally-Tuned Processing Units, Neural Computation, vol. 1, pp. 281-294, Jun. 1989.
• [268] Radial basis function network - Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Radial_basis_function_network. [Accessed: 05-Oct-2011].
• [269] M. de Berg, Computational geometry: algorithms and applications., 2. ed. Berlin: Springer Verlag, 2000.
• [270] C.B. Barber, D.P. Dobkin and H. Huhdanpaa, The Quickhull algorithm for convex hulls, ACM Transactions on Mathematical Software, vol. 22, no. 4, pp. 469-483, 1996.
• [271] D. Avis, How good are convex hull algorithms?, Computational Geometry, vol. 7, pp. 265-301, Apr. 1997.
• [272] Convex hull algorithms - Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Convex_hull_algorithms. [Accessed: 02-Mar-2012].
• [273] G.I. Nalbantov, P.J.F. Groenen and J.C. Bioch, Nearest Convex Hull Classi? cation, Econometric Institute, Rotterdam, EI 2006-50, Jan. 2007.
• [274] G.I. Nalbantov, J.C. Bioch and P.J.F. Groenen, Classification with Support Hyperplanes, in Machine Learning: ECML 2006, vol. 4212, J. Fürnkranz, T. Scheffer and M. Spiliopoulou, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 703-710.
• [275] X. Zhou and Y Shi, Nearest Neighbor Convex Hull Classification Method for Face Recognition, in Computational Science - ICCS 2009, vol. 5545, G. Allen, J. Nabrzyski, E. Seidel, G. van Albada, J. Dongarra and P. Sloot, Eds. Springer Berlin / Heidelberg, 2009, pp. 570-577.
• [276] P. Pudil and P. Somol, Current Feature Selection Techniques in Statistical Pattern Recognition, in Computer Recognition Systems, vol. 30, M. Kurzyński, E. Puchała, M. Woźniak, and A. Żołnierek, Eds. Springer Berlin / Heidelberg, 2005, pp. 53-68.
• [277] P. Pudil, J. Novovičová and J. Kittler, Floating search methods in feature selection, Pattern Recognition Letters, vol. 15, no. 11, pp. 1119-1125, Nov. 1994.
• [278] Qhull code for Convex Hull, Delaunay Triangulation, Voronoi Diagram and Halfspace Intersection about a Point. [Online]. Available: http://www.qhull.org/. [Accessed: 05-Oct-2011].
• [279] Weka 3 – Data Mining with Open Source Machine Learning Software in Java. [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/ [Accessed: 05-Oct-2011].
• [280] J. Nowak, Master of Engineering Thesis: Application of MPEG 7 Texture, Shape and Colour Descriptors for Detection of Pathological Changes of Gastrointestinal System in Wireless Endoscopy Images. Technical University of Łódź: 2011.
• [281] k-means clustering – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/K-means [Accessed: 06-Oct-2011].
• [282] Cluster analysis – Wikipedia, the free encyclopedia. [Online]. Available: http://en.wikipedia.org/wiki/Cluster_analysis [Accessed: 06-Oct-2011].
• [283] MaZda Web Site. [Online]. Available: http://www.eletel.p.lodz.pl/programy/mazda/ [Accessed: 02-Mar-2012].
• [284] Wspomaganie diagnostyki medycznej przewodu pokarmowego z zastosowaniem analizy komputerowej sekwencji obrazów uzyskiwanych z endoskopu kapsuł-kowego. [Online]. Available: http://212.191.90.111/joomla/ [Accessed: 06-Oct-2011].
• [285] P.M. Szczypiński, Strona WWW – Downloads. [Online]. Available: http://www.eletel.p.lodz.pl/pms/prog_en.html. [Accessed: 02-Mar-2012].
• [286] A. Karargyris, A Novel Synergistic Diagnosis Methodology for identifying Abnormalities in Wireless Capsule Endoscopy videos, Wright State University, 2010.
• [287] A. Khoshroo, A. Keyhani, R.A. Zoroofi, S. Rafiee, Z. Zamani and M.R. Alsharif, Classification of Pomegranate Fruit using Texture Analysis of MR Images, Agricultural Engineering International: CIGR Journal, vol. XI, 2009.
• [288] S. Kumar and G.S. Mittal, Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network, Food and Bioprocess Technology, vol. 3, pp. 741-751, Jul. 2008.
• [289] J. Letal, D. Jirak, L. Suderlova and M. Hajek, MRI texture analysis of MR images of apples during ripening and storage, Lebensmittel-Wissenschaft und-Technologie, vol. 36, no. 7, pp. 719-727, 2003.
• [290] G. Collewet, M. Strzelecki and F. Mariette, Influence of MRI acquisition protocols and image intensity normalization methods on texture classification, Magnetic Resonance Imaging, vol. 22, no. 1, pp. 81-91, 2004.