The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.
Khurd et al. REF have developed a texture classification system for automatic and reproducible Gleason grading.
Computer-aided gleason grading of prostate cancer histopathological images using texton forests
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Computer Science Medicine