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Review
. 2021 Dec 3;46(1):7.
doi: 10.1007/s10916-021-01786-9.

Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review

R Rashmi  1 , Keerthana Prasad  2 , Chethana Babu K Udupa  3
Affiliations
Review

Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review

R Rashmi et al. J Med Syst. .

Abstract

Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

Keywords: Breast cancer; Deep learning; H&E Stains; Histopathological images; Image classification; Image segmentation; Machine learning.

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Conflict of interest statement

The authors declare that they have no conflict of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This article does not contain any studies with human participants or animals performed by any of the authors.

Figures

Fig. 1
Fig. 1
Histopathological types of breast cancer [10]
Fig. 2
Fig. 2
Microscopic patterns of benign breast tumor (a) Fibroadenoma (Intracanalicular pattern), (b) Fibroadenoma (Pericanalicular pattern) (c) Phyllodes tumor (d) Intraductal papilloma
Fig. 3
Fig. 3
Microscopic patterns of Noninvasive (In situ) carcinoma (a) Intraductal carcinoma (b) Lobular carcinoma
Fig. 4
Fig. 4
Microscopic patterns of Invasive carcinoma. (a) IDC (b) Invasive lobular carcinoma (c) Medullary carcinoma (d) Mucinous carcinoma (e) Papillary carcinoma (f) Tubular carcinoma (g) Adenoid cystic carcinoma (h) Secretory carcinoma (i) Inflammatory carcinoma (j) Carcinoma with metaplasia
Fig. 5
Fig. 5
Histopathological image challenges. Figure (a) shows an example of artefact, (b) shows an example of tissue folding, (c) shows an example of thick sectioning, (d) shows an example of air bubbles, (e) shows an example of thin sectioning and (f) shows an example of blurring
Fig. 6
Fig. 6
Sample images to demonstrate the colour shade and illumination variations
Fig. 7
Fig. 7
An example of (a) normal nuclei, (b) prominent nucleoli, (c) hyperchromatic nuclei (d) cancerous nuclei, (e) mitotic nuclei, (f) lymphocyte, (g) clustered nuclei and (h) overlapping nuclei
Fig. 8
Fig. 8
Illustration of segmentation methods used in literature
Fig. 9
Fig. 9
Illustration of various classifiers used in the literature
Fig. 10
Fig. 10
Illustration of the database used in the literature
Fig. 11
Fig. 11
Distribution of image samples for different categories of diseases in BreakHis dataset
Fig. 12
Fig. 12
Distribution of image samples for benign and malignant cases in BreakHis dataset
Fig. 13
Fig. 13
Description of various CNN architectures used for binary and multi-class classification
See this image and copyright information in PMC

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