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Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization

Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization

Ichrak Khoulqi, Najlae Idrissi, Muhammad Sarfraz
Copyright: © 2020 |Pages: 16
ISBN13: 9781799844440|ISBN10: 1799844447|ISBN13 Softcover: 9781799852049|EISBN13: 9781799844457
DOI: 10.4018/978-1-7998-4444-0.ch009
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MLA

Khoulqi, Ichrak, et al. "Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization." Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, IGI Global, 2020, pp. 162-177. https://doi.org/10.4018/978-1-7998-4444-0.ch009

APA

Khoulqi, I., Idrissi, N., & Sarfraz, M. (2020). Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization. In M. Sarfraz (Ed.), Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies (pp. 162-177). IGI Global. https://doi.org/10.4018/978-1-7998-4444-0.ch009

Chicago

Khoulqi, Ichrak, Najlae Idrissi, and Muhammad Sarfraz. "Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, 162-177. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-4444-0.ch009

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Abstract

Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.

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