Abstract
Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
Highlights
Breast cancer represents one of the foremost factors behind the death of women worldwide
(i) In this survey, we provide a comprehensive study of state-of-art methods for mammogram image segmentation from 1999-2021 (ii) First, we introduce segmentation pipeline used in mammogram images (iii) Second, we discuss the most frequently used filters to remove noise from mammogram images (iv) Third, we discuss the publicly and privately available databases for mammogram images and its segmentation metrics and classification (v) we investigate the most frequently used techniques for classical segmentation, machine learning segmentation, and deep learning segmentation
We have reviewed previous works from 1999 to 2021 which are related to mammogram segmentation based on masses and microcalcifications found in mammogram images
Summary
Breast cancer represents one of the foremost factors behind the death of women worldwide. We reviewed a number of previous research papers related to mammogram image segmentation to detect, extract, or classify breast cancers. The quality of segmentation is based on the filter used to remove artifacts from the mammogram images It has been a big challenge for researchers to find mammogram images, as from the reviewed papers that no conclusion was made as to which database is frequently used for mammogram image segmentation. The generic computer-aided diagnosis system which includes segmentation, feature extraction, and classification stages [66] has been developed to assist medical experts in breast cancer classification. It is worth noting that the scope of this manuscript is to highlight segmentation techniques for breast cancer detection using mammogram images based on masses and microcalcifications. These limitations will give insight to the reader to help select the appropriate segmentation technique
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