To enable an effective treatment, the prostate cancer (PCa) must be detected early enough. Unfortunately, the diagnostic methods are insufficient. The hope for improve the PCa diagnosis lies in the perfusion computed tomography (p-CT) method. However, the p-CT prostate images are not easy to interpret.
The presented work describes the technique of computational analysis of such images using the textural features of the Haralick’s co-occurrence matrices. The research based on the material from over 50 patients concentrated on selection of proper preprocessing procedures, optimal feature space and the best decision function. A serious problem was also to choose regions of interest - especially important areas in the gland.
It seems that the improvement of detectability of PCa with the p-CT technology is possible by creating a dedicated computational system to CT scanners, that could point out the cancerous lesions automatically, faster, and more reliable than in traditional methods.