Mammogram Image Segmentation Using Rough Clustering

This paper motivated to design and develops an automatic model for multi-class breast tissue segmentation in breast mammogram images. Various breast tissues are categorized by a novel texture features such as PTPSA-[Piecewise Triangular Prism Surface Area], intensity difference and regular-intensity in mammogram images. Using CRF-[Classical Random Forest] method segmentation and classification of the features can be obtained in mammogram images. The input image feature values are compared with the ground-truth values for confirming the true positive rate of the proposed approach. Efficacy of abnormal breast tissue segmentation is evaluated using publicly available MIAS training dataset. Performance evaluation of the proposed approach can be obtained by comparing the simulation output with the ground truth data. The accuracy of the proposed approach reaches up to 97% for MIAS database.

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Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

Chaudhury

Krishna²,

Gupta

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework’s development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

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Mammogram Image Segmentation Using Rough Clustering

Abstract: The mammography is the most effective procedure to diagnosis the breast cancer at an early stage. This paper proposes mammogram image segmentation using Rough K-Means (RKM)

Cited by 5 publications

References 17 publications

LabVIEW-Powered Integrated Diagnosis Tool for Precision Breast Cancer Detection: A Multifaceted Approach

LabVIEW-Powered Integrated Diagnosis Tool for Precision Breast Cancer Detection: A Multifaceted Approach

Multi-class abnormal breast tissue segmentation using texture features

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

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