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Open AccessJournal ArticleDOI

Histopathological image analysis using image processing techniques : an overview

Amoli Belsare, +1 more
- 31 Aug 2012 - 
- Vol. 3, Iss: 4, pp 23-36
TLDR
This paper reviews and summarizes the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.
Abstract
This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue. In this paper we reviews and summarize the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.

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Detection of breast cancer on digital histopathology images: Present status and future possibilities

TL;DR: This article reviews and summarizes the applications of digital image processing techniques on histopathological images for the detection of breast cancer and discusses its future possibilities.
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A decision support system for Acute Leukaemia classification based on digital microscopic images

TL;DR: This study presents a decision support system that includes the panel selection, segmentation using K-means clustering to identify the leukemia cells and features extraction, and image refinement that yielded promising results and warrants further research.
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Deep transfer with minority data augmentation for imbalanced breast cancer dataset

TL;DR: Experiments on the benchmark BreakHis dataset for different magnification factors validate the efficiency of the proposed deep transfer learning approach due to the high scores achieved as compared to the state-of-the-art deep networks.
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Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images

TL;DR: Clinicians to receive second opinion from the CAD system for early diagnosis, and researchers to analyze and enhance the existing state-of-art techniques used in CAD system, which may further reduce the gap of variability between intra and inter observer are beneficial.
Journal ArticleDOI

A Novel Polar Space Random Field Model for the Detection of Glandular Structures

TL;DR: This work converts the image from Cartesian space to polar space and introduces a novel random field model to locate the possible boundary of a gland and develops a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland.
References
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Book ChapterDOI

I and J

Journal ArticleDOI

Histopathological Image Analysis: A Review

TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.
Journal ArticleDOI

Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images

TL;DR: This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas, and presents an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.
Journal ArticleDOI

Automatic seeded region growing for color image segmentation

TL;DR: An automatic seeded region growing algorithm for color image segmentation that can produce good results as favorably compared to some existing algorithms.
Proceedings ArticleDOI

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

TL;DR: The utility of the glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of prostate cancer, breast cancer, and breast cancer specimens is demonstrated by distinguishing between cancerous and benign breast histology specimens.
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