OpenLB
Open Library of Bioscience

The use of nuclear grade as a prognostic indicator in breast cancer has been limited by its poor interobserver reproducibility. Automated cell 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 order 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, such as granularity, contrast, symmetry, peripheral chromatin clumping, and number and shape of nucleoli. These models were analyzed on the CAS-100 image analysis system using software capable of measuring 22 Markovian texture features at 20 levels of pixel resolution (grain). We were able to show that Markovian analysis performed well in discriminating between degrees of chromatin granularity (finely vs. coarsely clumped), amount of contrast (vesicular change), thickness of peripheral chromatin and number of nucleoli. Of the 22 Markovian features, 10 were selected as optimal for discriminating between the above chromatin patterns. Similar optimal Markovian features were found when measurements were performed on captured images of breast cancer cells. The use of these selected Markovian texture features may allow a more rational approach to the use of image analysis for cell classification.