Texture analysis of liver hydatid cyst

Images which are obtained in clinical radiology are generally evaluated visually. Some information which is available in the images, but not possible to be seen visually can be useful for diagnosis of some diseases. Cyst hydatid which is a parasitic liver disease is still an important health problem in countries where animal breeding is widespread. In this study, we aimed at producing some objective measures using image analysis, which will be of assistance in the diagnosis of cyst hydatid. The prevalence rate of this condition is relatively high in Turkey. In order to differentiate between regions of liver with cyst hydatid and healthy parenchymal tissues, we have used second order texture features computed from gray level cooccurrence matrix of liver CT images. We have then used these features from the two groups in designing a classifier using probabilistic neural network. Our results indicate that the texture features computed from the gray level cooccurrence matrix do not constitute a good candidate to be used in classification and/or diagnosis of liver tissue as normal or cystic. This must be due to homogeneity of these two tissue types within themselves.