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

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 mammogra...

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Bibliographic Details
Published in:International journal of healthcare information systems and informatics 2022-01, Vol.17 (1), p.1-14
Main Authors: Tammineedi, Venkata Satya Vivek, C, Raju, D, Girish Kumar, Yalla, Venkateswarlu
Format: Article
Language:English
Subjects:
Analysis
Mammography
Methods
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Summary: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.
ISSN:1555-3396
1555-340X
DOI:10.4018/IJHISI.305236