Improvement of Segmentation Efficiency in Mammogram Images Using Dual-ROI Method

Venkata Satya Vivek Tammineedi, Raju C., Girish Kumar D. and Venkateswarlu Yalla
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Venkata Satya Vivek Tammineedi: Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, AP, INDIA,
Raju C.: Sri Venkateswara Engineering College, India
Girish Kumar D.: Shri Vihnu Engineering College for Women, India
Venkateswarlu Yalla: BVC College of Engineering, India

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2022, vol. 17, issue 1, 1-14

Abstract: Mammogram segmentation utilizing multi-region of intrigue is a standout amongst the most rising exploration territory in the medical image analysis. The steps engaged with the research are grouped into two kinds: 1) segmentation of mammogram images and 2) extraction of texture features from mammogram images. To overcome these difficulties, a compelling technique is proposed in this paper that comprises of three phases. In the principal arrangement, mammogram images from INbreast database are selected and improved utilizing Laplacian filtering. At that point, the pre-processed mammogram images are utilized for segmentation utilizing modified adaptively regularized kernel-based fuzzy C means (M-ARKFCM). After segmentation, statistical texture FE is connected for recognizing the patterns of cancer and non-cancer regions in mammogram images. Finally, the experimental outcome demonstrated that the proposed approach enhanced the segmentation efficiency by methods of statistical parameters contrasted with the existing operating procedures.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhisi0:v:17:y:2022:i:1:p:1-14

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