title: Toward automatic mitotic cell detection and segmentation in multispectral histopathological images.
abstract: The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 × 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81.5% sensitivity rate and 33.9% precision rate in terms of detection performance, and 89.3% sensitivity rate and 87.5% precision rate in terms of segmentation performance.
authors.forename: Cheng
authors.initials: C
authors.lastname: Lu
authors.forename: Mrinal
authors.initials: M
authors.lastname: Mandal
mesh_terms: D001943:Breast Neoplasms / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure; D016002:Discriminant Analysis; D005260:Female; D006651:Histocytochemistry / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008938:Mitosis; D060787:Neoplasm Grading / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity