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

Yazan M Alomari,Reena Rahayu Mdzin,Siti Norul Huda Sheikh Abdullah,Khairuddin Omar

doi:10.1155/2015/673658

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.

Highlights

Cancer is a leading cause of death worldwide
This paper proposed a method that adaptively localizes focus point regions from whole-slide Ki-67-stained histopathology images
The random patch probabilistic density method can localize the tissue based on the density feature of an unknown number of clusters

Summary

Introduction

Cancer is a leading cause of death worldwide. In Malaysia, more than 30,000 deaths from cancer have been reported annually with most of these cases being diagnosed at an advanced stage [1].The analysis of microscopy images is extremely important in both the medical and computer science fields. Analysis of whole-slide tissue images is an important part of cancer diagnosis. Manually selected tissue slide focus point regions do not capture the complete information available to pathologists during initial microscopic analysis. If imaging increases the suspicion of a brain tumor, a brain biopsy is usually performed. A biopsy is a procedure that involves the removal of a small portion from the tumor area so that the cells or tissues can be examined [2]. This sample is treated and sliced in a pathology laboratory, and the histological structure of the tissue cells is examined under the microscope by a pathologist. Usually each diagnostic process involves staining the specimen with specific dyes [3]

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