Abstract
The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.
Highlights
Breast Cancer is the most prevalent type of cancer in women worldwide [1]
A semi-quantitative assessment of the breast cancer prognosis is well established by the Bloom-Richardson grading system [2] which defines the scoring of three morphological features of the suspicious tissue: 1) percentage of tubule formation, 2) degree of nuclear pleomorphism, and 3) mitotic cell count
Given the importance and challenges of segmenting cancerous nuclei in breast histopathology images, this paper proposes a novel segmentation framework that implements tensor voting followed by Loopy Belief Propagation (LBP) on a Markov Random Field (MRF) for nuclei delineation in breast cancer histopathology images
Summary
Breast Cancer is the most prevalent type of cancer in women worldwide [1]. Current breast cancer clinical practice and treatment mainly relies on the evaluation of the disease’s prognosis. Automatic Nuclei Segmentation in Breast Cancer Histopathology Images examination of the biopsy specimen of the tissue under microscope which has a substandard reproducibility [3]. [2] had suggested the use of image analysis of the breast histology tissue for accurate estimation of nuclei size and shape differences. With relevance to these propositions, the advancements in digital pathology [5] and the advent of fast digital slide scanners [6] had simplified the digitization of histopathology slides and opened the possibility to apply image analysis techniques. Histopathology image datasets are available online from various open sources, such as the UCSB dataset from Center for Bio-Image Informatics, University of California, Santa Barbara [7], MITOS-ATYPIA grand challenge dataset [8], and the Assessment of Mitosis Detection Algorithms (AMIDA13) dataset [9]
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