Discriminatory Power of Co-Occurrence Features in Perfusion CT Prostate Images

• Autorzy: Śmietański J. , Tadeusiewicz R.
• Języki publikacji: EN

Abstrakty

EN

This paper presents an algorithm used to improve the effectiveness of early prostate cancer (PCa)detection. The necessity for using such a computational method lies in the fact that although perfusion computed tomography (p-CT) is considered a good technique for the detection of early PCa, the p-CT prostate images are very difficult to interpret manually by radiologists. We hereby propose a methodology for computational analysis of p-CT prostate images based on textural coefficients derived from co-occurrence matrices and their 21 coefficients. The selection of only a few of the considered features ensures the necessary balance between matching set of already known images and new, not yet clear cases. The proposed algorithm for automatic differentiation of the healthy area of the image from the cancerous region was tested on a set of 59 prostate images. Although the results were not entirely satisfactory (86% correct recognitions), this method may be considered as the base for the development of a better algorithm.

Słowa kluczowe

EN

prostate cancer; perfusion computed tomography; medical image analysis; pattern recognition

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2010
• Tom: Vol. 19, No. 2
• Strony: 185--199
• Opis fizyczny: Bibliogr. 24 poz., wykr.

Twórcy

autor

Śmietański J.

autor

Tadeusiewicz R.

• Jagiellonian University, ul. Łojasiewicza 6, 30-438 Kraków,

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