title: Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images.
abstract: Histopathological grading of cancer not only offers an insight to the patients' prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in F1 score on more than 450 histopathological images at 40× magnification.
authors.forename: Angshuman
authors.initials: A
authors.lastname: Paul
authors.forename: Dipti Prasad
authors.initials: DP
authors.lastname: Mukherjee
mesh_terms: D001943:Breast Neoplasms / Q000473:pathology*; D005260:Female; D006651:Histocytochemistry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008938:Mitosis / Q000502:physiology*; D060787:Neoplasm Grading / Q000379:methods*