Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue

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2 Citations (Scopus)

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.

Original languageEnglish
Article number673658
JournalComputational and Mathematical Methods in Medicine
Volume2015
DOIs
Publication statusPublished - 22 Feb 2015

Fingerprint

Brain Tumor
Brain Neoplasms
Patch
Tumors
Brain
Tissue
Pathology
Clustering Methods
Cluster Analysis
Microscopes
Fuzzy C-means Clustering
Pooling
K-means
Number of Clusters
Inaccurate
Proliferation
Digital Image
False Positive
Microscope
Eagles

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Immunology and Microbiology(all)

Cite this

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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.",
author = "Alomari, {Yazan M.} and {Sheikh Abdullah}, {Siti Norul Huda} and {Md Zain}, {Reena Rahayu} and Khairuddin Omar",
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AU - Omar, Khairuddin

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