Prostate cancer grading: Gland segmentation and structural features

Highlights? A study about prostate cancer grading in digital histology. ? We analyze the prostate tissue structure by segmenting glands from the tissue. ? Structural features are extracted and used for the classification of a tissue pattern. ? A hierarchical scheme employing both the proposed method and the texture-based method. In this paper, we introduce a novel approach to grade prostate malignancy using digitized histopathological specimens of the prostate tissue. Most of the approaches proposed in the literature to address this problem utilize various textural features computed from the prostate tissue image. Our approach differs in that we only focus on the tissue structure and the well-known Gleason grading system specification. The color space representing the tissue image is investigated and basic components of the prostate tissue are detected. The components and their structural relationship constitute a complete gland region. Tissue structural features extracted from gland morphology are used to classify a tissue pattern into three major categories: benign, grade 3 carcinoma and grade 4 carcinoma. Our experiments show that the proposed method outperforms a texture-based method in the three-class classification problem and most of the two-class classification problems except for the grade 3 vs grade 4 classification. Based on these results, we propose a hierarchical (binary) classification scheme which utilizes the two methods and obtains 85.6% accuracy in classifying an input tissue pattern into one of the three classes.