Breast Abnormality Boundary Extraction in Mammography Image Using Variational Level Set and Self-Organizing Map (SOM)

Noor Ain Syazwani Mohd Ghani, Abdul Kadir Jumaat, Rozi Mahmud, Mohd Azdi Maasar, Farizuwana Akma Zulkifle and Aisyah Mat Jasin
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Noor Ain Syazwani Mohd Ghani: School of Mathematical Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
Rozi Mahmud: Radiology Department, Faculty of Medicine and Health sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Mohd Azdi Maasar: Mathematical Sciences Studies, College of Computing, Informatics and Media, Seremban Campus, Universiti Teknologi MARA (UiTM) Negeri Sembilan Branch, Seremban 70300, Negeri Sembilan, Malaysia
Farizuwana Akma Zulkifle: Computing Sciences Studies, College of Computing, Informatics and Media, Kuala Pilah Campus, Universiti Teknologi MARA (UiTM) Negeri Sembilan Branch, Kuala Pilah 72000, Negeri Sembilan, Malaysia
Aisyah Mat Jasin: Computing Sciences Studies, College of Computing, Informatics and Media, Pahang Branch, Raub Campus, Universiti Teknologi MARA (UiTM), Raub 27600, Pahang, Malaysia

Mathematics, 2023, vol. 11, issue 4, 1-20

Abstract: A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis. In computer vision, this method is also known as image segmentation. The variational level set mathematical model has been proven to be effective for image segmentation. Several selective types of variational level set models have recently been formulated to accurately segment a specific object on images. However, these models are incapable of handling complex intensity inhomogeneity images, and the segmentation process tends to be slow. Therefore, this study formulated a new selective type of the variational level set model to segment mammography images that incorporate a machine learning algorithm known as Self-Organizing Map (SOM). In addition to that, the Gaussian function was applied in the model as a regularizer to speed up the processing time. Then, the accuracy of the segmentation’s output was evaluated using the Jaccard, Dice, Accuracy and Error metrics, while the efficiency was assessed by recording the computational time. Experimental results indicated that the new proposed model is able to segment mammography images with the highest segmentation accuracy and fastest computational speed compared to other iterative models.

Keywords: active contour; mammography images; selective segmentation; SOM; variational level set (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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