Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist.
Kalaw Emarene Mationg
We present an AdaBoost-based Ensemble Learning for supporting automated Gleason grading of prostate adenocarcinoma (PRCA). The method is able to differentiate Gleason patterns 4–5 from patterns 1–3 as the patterns 4-5 are correlated to more aggressive disease while patterns 1-3 tend to reflect more favorable patient outcome. This method is based on various feature descriptors and classifiers for multiple color channels, including color channels of red, green and blue, as well as the optical intensity of hematoxylin and eosin stainings.