Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology.
Breast cancer is the second most frequent cancer. The reference process for breast cancer prognosis is Nottingham grading system. According to this system, mitosis detection is one of the three important criteria required for grading process and quantifying the locality and prognosis of a tumor. Multispectral imaging, as relatively new to the field of histopathology, has the advantage, over traditional RGB imaging, to capture spectrally resolved information at specific frequencies, across the electromagnetic spectrum. This study aims at evaluating the accuracy of mitosis detection on histopathological multispectral images. The proposed framework includes: selection of spectral bands and focal planes, detection of candidate mitotic regions and computation of morphological and multispectral statistical features. A state-of-the-art of the methods for mitosis classification is also provided. This framework has been evaluated on MITOS multispectral dataset and achieved higher detection rate (67.35%) and F-Measure (63.74%) than the best MITOS contest results (Roux et al., 2013). Our results indicate that the selected multispectral bands have more discriminant information than a single spectral band or all spectral bands for mitotic figures, validating the interest of using multispectral images to improve the quality of the diagnostic in histopathology.
University Joseph Fourier, Grenoble 1, France; French National Center for Scientific Research, IPAL UMI 2955, Singapore. Electronic address: humayun.irshad@gmail.com.
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Irshad
CoSMo Software, Boston, MA, USA; Temasys Communications, Singapore.
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Gouaillard
University Joseph Fourier, Grenoble 1, France; French National Center for Scientific Research, IPAL UMI 2955, Singapore.
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Roux
Sorbonne Universités, UPMC Univ Paris 6, France; French National Center for Scientific Research, IPAL UMI 2955, Singapore.
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Racoceanu
D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology / Q000503:physiopathology*; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D008938:Mitosis; D010363:Pattern Recognition, Automated / Q000379:methods*
Breast cancer; Classification; Features extraction; Histopathology; Multispectral images; Object detection; Spectral bands selection; Texture characterization