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Tytuł artykułu

Using a priori data for segmentation anatomical structures of the brain

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Wykorzystanie danych a priori do segmentacji anatomicznych struktur mózgu
Języki publikacji
EN
Abstrakty
EN
The paper presents a method for providing a segmentation of the anatomical structures of the brain at the image. The proposed method of segmentation makes the analysis of tomographic slices based on the stage of the provisional classification. Furthermore, for the segmentation of brain CT slices, it is proposed to use as the information about the position and the shape of structures and the X-ray density.
PL
Artykuł prezentuje metodę segmentacji struktur anatomicznych mózgu. Przedstawiona metoda segmentacji wykorzystuje etap wstępnej anlizy przekrojów tomograficznych. Dodatkowo informacji uzyskana na etapie segmentacji obrazów CT mózgu, może być wykorzystana do określania kształtu o położenia struktur mózgowych, jak również określenia wiązki promieniowania X podczas zabiegu operacyjnego.
Rocznik
Strony
102--105
Opis fizyczny
Bibliogr. 22 poz., rys., wykr.
Twórcy
  • Kharkiv National University of Radio Electronics, Dept. of Biomed. Eng, Nauky Ave, 14, Kharkiv, Ukraine
  • Kharkiv National University of Radio Electronics, Dept. of Biomed. Eng, Nauky Ave, 14, Kharkiv, Ukraine
  • Vinnytsia National Medical University by M.Pirogov, Pirogova Str., 56, Vinnytsia, Ukraine
  • Vinnitsa National Technical University, 95 Khmelnytske shose, 21021, Vinnytsya, Ukraine
autor
  • Lublin University of Technology, Dept of Electronics and Information Tehnologies, Nadbystzycka 38a, 20-618 Lublin Poland
autor
  • D.Serikbayev East Kazakhstan State Technical University
Bibliografia
  • [1] Irhebhude M.E., Edirisinghe E.A., Personnel Recognition in the Military using Multiple Features, International Journal ofComputer Vision and Signal Processing, 5(1) (2015), 23–30
  • [2] Chen Y.-L., Nighttime Vehicle Light Detection on a Moving Vehicle using Image Segmentation and Analysis Techniques, WSEAS Transactions on Computers, 8(3) (2009), 506–515
  • [3] Hadi, R.A. Sulong, G., George L.E., Vehicle Detection and Tracking Techniques: A Concise Review, Signal & Image Processing: An International Journal, 5(1) (2014), 1–12
  • [4] Koundinya G.G., Jaikumar G., Akash, N.R., Subramanian M.S.V., Survey on Digital Image Processing in Sports, Research Journal of Applied Sciences, Engineering and Technology, 4(24) (2012), 5552–5556
  • [5] Kannan P., Ramakrishnan R., Development of Human Pose Models for Sport Dynamics Analysis using Video Image Processing Techniques, International Journal of Sports Science and Engineering, 6(4) (2012), 232–238
  • [6] Ghosh P., Mitchel l , M., Prostate Segmentation on Pelvic CT Images Using a Genetic Algorithm, Proc. SPIE 6914, (2008)
  • [7] Zayane O., Jouini B., Mahjoub M.A., Automatic liver segmentation method in CT images, Canadian Journal on Image Processing & Computer Vision, 8(2) (2011), 92–95
  • [8] Campadeli P., et al., Automatic Abdominal Organ Segmentation from CT Images, Electronic Letters on Computer Vision and Image Analysis, 8(1) (2009), 1–14
  • [9] Datta S., Narayana P.A., A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis, NeuroImage: Clinical, 2 (2013) 184–196
  • [10] El-Melegy M.T., Mokhtar H.M., Tumor segmentation in brain MRI using fuzzy approach with class center priors, EURASIP Journal on Image and Video Processing, (2014), 21
  • [11] Lenkiewicz P., et. al., The whole mesh deformation model: a fast image segmentation method suitable for effective parallelization, EURASIP Journal on Advances in Signal Processing, (2013), 55
  • [12] Llado X., et al . , Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches, Information Sciences, 186 (2012), 164–185
  • [13] Skalski A., et al., Automatic features generation based on 3D anisotropic SIFT for Computed Tomography data segmentation, Przegląd Elektrotechniczny, R91 (5) (2015), 25-28
  • [14] Aljarab, P. et al., Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy, NeuroImage 46 (2009), 726–738
  • [15] Cabezas M., Oliver A., Llado X., Freixenet J., Cuadra, M.B., A review of atlas-based segmentation for magnetic resonance brain images, Computer Methods and Programs in Biomedicine, i04 (2011), e158-e177
  • [16] Węgliński T., Fabijańska T., On cerebrospinal fluid segmentation from CT brain scans using interactive graph cuts, IAPGOŚ, 4b (2012), 7-9
  • [17] Wachinger C., Fritscher K., Sharp G., Golland P. Contour-Driven Atlas-Based Segmentation, Transaction on Medical Imaging, 34(12) (2015), 2492–2505
  • [18] Shattuck D.W., et al., Online resource for validation of brain segmentation methods, Neuroimage 45 (2009), 431–439
  • [19] Atkins M.S., et. al., Difficulties of T1 Brain Image Segmentation, Proc. of SPIE Conference on Medical Imaging, (2002), 1837–1844
  • [20] Avrunin O., Tymkovych M., Kononenko T., Capabilities to Visualize the Operating Region of Surgical Inter-vention Relatively to Cranial Landmarks for Neuronavi-gation, EUREKA: Physical Sciences and Engineering, 1 (2016), 21–30
  • [21] Avrunin O.G., et al., Classification of CT-brain slices based on local histograms, Proc. SPIE, Optical Fibers and Their Applications, 9816 (2015)
  • [22] Moeller T.B., Reif E., Pocket Atlas of Sectional Anatomy. Computed Tomography and Image Resonance Imaging. Head and Neck, (2008), 272
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-47041064-5bd0-4ba1-adf0-919bf2e86a90
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