title: Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.
abstract: Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.
authors: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors: Yazan M
authors: YM
authors: Alomari
authors: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors: Siti Norul Huda
authors: 0000-0002-2602-7805
authors: SN
authors: Sheikh Abdullah
authors: Department of Pathology, UKM Medical Center, Universiti Kebangsaan Malaysia, 56000 Cheras, Kuala Lumpur, Malaysia.
authors: Reena Rahayu
authors: RR
authors: MdZin
authors: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors: Khairuddin
authors: 0000-0003-1794-019X
authors: K
authors: Omar
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism; D001932:Brain Neoplasms / Q000378:metabolism*; D049109:Cell Proliferation; D016000:Cluster Analysis; D019295:Computational Biology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D019394:Ki-67 Antigen / Q000378:metabolism*; D008853:Microscopy; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D012984:Software
chemical_list: D019394:Ki-67 Antigen