Fundamentals of digital image processing
Fundamentals of digital image processing
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A boosting cascade for automated detection of prostate cancer from digitized histology
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Texture representations using subspace embeddings
Pattern Recognition Letters
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
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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.