title: Structure and context in prostatic gland segmentation and classification.
abstract: A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These features are used to classify a gland segment into one of the three classes: artifact, normal gland and cancer gland. On a dataset of 48 images at 5x magnification (which includes 525 artifacts, 931 normal glands and 1,375 cancer glands), we achieved the following classification accuracies: 93% for artifacts v. true glands; 79% for normal v. cancer glands, and 77% for discriminating all three classes. The proposed method outperforms state of the art methods in terms of segmentation and classification accuracies and computational efficiency.
authors.affiliation: Michigan State Unversity, East Lansing, MI 48824, USA. nguye231@cse.msu.edu
authors.forename: Kien
authors.initials: K
authors.lastname: Nguyen
authors.forename: Anindya
authors.initials: A
authors.lastname: Sarkar
authors.forename: Anil K
authors.initials: AK
authors.lastname: Jain
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003593:Cytoplasm / Q000378:metabolism; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008297:Male; D015233:Models, Statistical; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000473:pathology*; D012984:Software