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Texture feature extraction in liver CT image analysis
Języki publikacji
Abstrakty
W pracy przedstawiono nową metodę opisu tekstur, przystosowaną do analizy grupy obrazów, przedstawiających na różne sposoby ten sam fragment organu. Charakteryzując obszary zainteresowania, uwzględniono nie tylko cechy teksturalne wyliczone na ich podstawie, ale również ich zależność od warunków pozyskiwania obrazów. Zaproponowano kilka sposobów konstrukcji przestrzeni parametrów odzwierciedlających zmianę tekstury, która zachodzi pod wpływem zmian warunków akwizycji. Proponowaną metodę zweryfikowano doświadczalnie w klasyfikacji obrazów tomograficznych wątroby. Rozpoznawano cztery typy tkanki, dla każdego przypadku rozważono trzy momenty akwizycji, związane z obecnością i propagacją środka kontrastującego. Wyniki uzyskane przy użyciu różnych zestawów cech teksturalnych i klasyfikatora w postaci dipolowych drzew decyzyjnych pokazują, że uwzględnienie zmian tekstury pod wpływem propagacji środka kontrastującego znacznie poprawia diagnozę.
In the work, a new method of texture characterization from multiple scan series is presented. Images with the same slice position, acquired at different conditions, are analyzed simultaneously. Thereby not only texture characteristics of the considered region of interest are taken into account, but also their variations over the different acquisition moments. A few approaches to description of these variations were proposed. They were applied in recognition of four types of hepatic tissue. Liver CT images were acquired during the three typical phases related to presence and propagation of contrast material. Experiments with various sets of texture parameters and dipolar decision tree as a classifier showed that simultaneous analysis of texture features derived from three subsequent acquisition moments could considerably improve the classification accuracy.
Rocznik
Tom
Strony
51--66
Opis fizyczny
Bibliogr. 31 poz., il., rys., tab.
Twórcy
autor
autor
autor
- Politechnika Białostocka, Wydział Informatyki, ul. Wiejska 45A, 15-351 Białystok
Bibliografia
- [1] Apostol L., Peyrin F., Yot S., Basset O., Odet C., Tabary J., Dinten J. M., Boller E., Boudousq V., Kotzki P. O.: A procedure for the evaluation of 2D radiographic texture analysis to assess 3D bone micro-architecture, Proc. of SPIE, Vol. 5370 Medical Imaging, 2004, pp. 195-206.
- [2] Bae R. Y., Belohlavek M., Greenleaf J. F., Seward J. B.: Myocardial contrast echocardiography: texture analysis for identification of nonperfused versus perfused myocardium, Echocardiography, 18(8), 2001, pp. 665-672.
- [3] Bobrowski L., Krętowski M.: Induction of multivariate decision trees by using dipolar criteria, LNCS Vol. 1910, Springer-Verlag, 2000, pp. 331-336.
- [4] Brown R. A., Zlatescu M. C., Cairncross J. G., Mitchell J. R.: Texture Analysis for Non-Invasive Identification of Brain Tumor Genotype from MRI, Proc. of the Fifth IASTED International Conference on Visualization, Imaging, and Image Processing, ACTA Press, 2005, pp. 459-464.
- [5] Bruno A., Collorec R., Bézy-Wendling J., Reuzé P., Rolland Y.: Texture analysis in medical imaging, W: Roux C., Coatrieux J. L. (edytorzy):Contemporary Perspectives in Three-dimensional Biomedical Imaging, IOS Press, 1997, pp. 133-164.
- [6] Chen C., Daponte J. S., Fox M. D.: Fractal feature analysis and classification in medical imaging, IEEE Transactions on Medical Imaging, 8, 1989, pp. 133-142.
- [7] Chen E. L., Chung P. C., Chen C. L., Tsai H. M., Chang C. I.: An automatic diagnostic system for CT liver image classification, IEEE Transactions on Biomedical Engineering, 45(6), 1998, pp. 783-794.
- [8] Clausi D. A., Jernigan M. E.: Designing Gabor filters for optimal texture separability, Pattern Recognition, 33, 2000, pp. 1835-1849.
- [9] Cross G. R., Jain A. K.: Markov random fields texture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1), 1985, pp. 25-39.
- [10] Duda D., Krętowski M., Bézy-Wendling J.: Texture characterization for Hepatic Tumor Recognition in Multiphase CT, Biocybernetics and Biomedical Engineering, 26(4), 2006, pp. 15-24.
- [11] Duda D., Krętowski M., Bézy-Wendling J.: Texture-based classification of hepatic primary tumors in multiphase CT, Proc. of 7th MICCAI, LNCS Vol. 3217, Springer-Verlag, 2004, pp. 1050-1051.
- [12] Galloway M. M.: Texture analysis using gray level run lengths, Computer Graphics and Image Processing, 4, 1975, pp. 172-179.
- [13] Gletsos M., Mougiakakou S. G., Matsopoulos G. K., Nikita K. S., Nikita A. S., Kelekis D.: A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a Neural Network classifier, IEEE Transactions on Information Technology in Biomedicine, 7(3), 2003, pp. 153-162.
- [14] Gonzalez R. C., Woods R. E.: Image Compression, W: Digital Image Processing, Wydanie drugie, Reading, MA: Addison-Wesley, 2002, pp. 409518.
- [15] Haralick R. M., Shanmugam K., Dinstein I.: Textural features for image classification, IEEE Transactions on Systems, Man and Cybernetics, 3, 1973, pp. 610-621.
- [16] Haralick R. M.: Statistical and structural approaches to texture, Proc. of the IEEE, 67, 1979, pp. 786-804.
- [17] Haralick R., Sternberg S. R., Zhuang X.: Image analysis using mathematical morphology, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), 1989, pp. 532-550.
- [18] Huang Y. L. Chen J. H. Shen W. C.: Diagnosis of Hepatic Tumors With Texture Analysis in Nonenhanced Computed Tomography Images, Academic Radiology, 13(6), 2006, pp. 713-720.
- [19] Husain S. A., Shigeru E.: Use of neutral networks for feature based recognition of liver region on CT images, Proc. of the IEEE Signal Processing Society Workshop, Vol. 2, 2000, pp. 831-840.
- [20] Krętowski M., Bezy-Wendling J., Duda D.: Classification of hepatic metastasis in enhanced CT images by dipolar decision tree, Proc. of 19th GRETSI, 2003, pp. 327-330.
- [21] Laws K. I.: Texture Energy Measures, Proc. of the DARPA Image Understanding Workshop, 1979, pp. 47-51.
- [22] Mala K., Sadasivam V.: Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network, Annual IEEE INDICON Conference, 2005, pp. 216 - 219.
- [23] Mallat S. G.: A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 1989, pp. 674-693.
- [24] Mir A. H., Hanmandlu M., Tandon S. N.: Texture analysis of CT-images, IEEE Engineering in Medicine and Biology, 5, 1995, pp. 781-786.
- [25] Mougiakakou S. G., Valavanis I., Mouravliansky N., Nikita A., Nikita K. S.: DIAGNOSIS: A Telematics Enabled System for Medical Image Archiving, Management and Diagnosis Assistance, Proc. of the IEEE IST International Workshop, 2006, pp. 43-48.
- [26] Nair A., Kuban B. D., Tuzcu E. M., Schoenhagen P., Nissen S. E., Vince D. G.: Coronary plaque classification with intravascular ultrasound radiofrequency data analysis, Circulation, 106(17), 2002, pp. 2200-2206.
- [27] Sariyanni C. P. A., Asvestas P., Matsopoulos G. K., Nikita K. S., Nikita A. S.Kelekis D.: A fractal analysis of CT liver images for the discrimination ofhepatic lesions: A comparative study, Proc. of the 23rd Annual EMBSInternational Conference, 2001, pp. 1557-1560.
- [28] Stoitsis J., Valavanis I., Mougiakakou S. G., Golemati S., Nikita A., Nikita K.:Computer aided diagnosis based on medical image processing and artificialintelligence methods, Nuclear Instruments and Methods in Physics Research,2006, pp. 591-595.
- [29] Valavanis I., Mougiakakou S. G., Nikita K. S., Nikita A.: Computer aideddiagnosis of CT focal liver lesions by an ensemble of neural network andstatistical classifiers, Proc. of the IEEE International Joint Conference onNeural Networks, Vol. 3, 2004, pp. 1929-1934.
- [30] Wei L., Yang Y., Nishikawa R. M. Jiang Y.: A study on several Machine-learning methods for classification of Malignant and benign clusteredmicrocalcifications, IEEE Trans. Med. Imaging, 24(3), 2005, pp. 371-380.
- [31] Weszka J. S. Dyer C. R., Rosenfeld A.: A Comparative Study of Texture Measures for Terrain Classification, IEEE Trans. Systems, Man, Cybernetics,6, 1976, pp. 269-285.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB2-0026-0031