CHROMATIN TEXTURE MEASUREMENT BY MARKOVIAN ANALYSIS - USE OF NUCLEAR-MODELS TO DEFINE AND SELECT TEXTURE FEATURES

The use of nuclear grade as a prognostic indicator in breast cancer ha s been limited by its poor interobserver reproducibility. Automated ce ll classification using digital image analysis is one approach to this problem. Nuclear chromatin distribution, an important feature used in nuclear grading, can be quantitated with texture analysis. Markovian analysis is one method of analyzing texture features that is available in a commercially available image analysis system, the CAS-100. In or der to select optimal Markovian features for use in nuclear grading of breast cancer, 16 nuclear models were created with computer graphics that demonstrated specific components of nuclear chromatin pattern, su ch as granularity, contrast, symmetry, peripheral chromatin clumping, and number and shape of nucleoli. These models were analyzed on the CA S-100 image analysis system using software capable of measuring 22 Mar kovian texture features at 20 levels of pixel resolution (grain). We w ere able to show that Markovian analysis performed well in discriminat ing between degrees of chromatin granularity (finely vs. coarsely clum ped), amount of contrast (vesicular change), thickness of peripheral c hromatin and number of nucleoli. Of the 22 Markovian features, 10 were selected as optimal for discriminating between the above chromatin pa tterns. Similar optimal Markovian features were found when measurement s were performed on captured images of breast cancer cells. The use of these selected Markovian texture features may allow a more rational a pproach to the use of image analysis for cell classification.