{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T13:18:56Z","timestamp":1787750336615,"version":"build-2784847793"},"reference-count":44,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Accurate segmentation of breast masses in mammographic images is a crucial step in computer-aided diagnosis (CAD) systems. However, mammograms are often affected by noise, low contrast, weak boundaries, and intensity inhomogeneity, which significantly challenge reliable segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>In this study, a novel variational active contour model is proposed by integrating spatially adaptive fractional-order enhancement with Hessian-based curvature weighting. A spatially varying fractional-order map is first derived from the image gradient to selectively enhance texture details and weak edges. Based on the enhanced image, second-order structural information is extracted through the Hessian matrix, whose eigenvalues are employed to construct an adaptive edge-stopping function sensitive to local curvature. To further address intensity inhomogeneity, a local region-fitting energy term inspired by the region-scalable fitting model is incorporated into the formulation. The proposed model is implemented within a level-set framework, ensuring numerical stability and topological flexibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The proposed method was evaluated on two publicly available mammogram datasets, namely INbreast and CBIS-DDSM. Quantitative comparisons using the Dice Similarity Coefficient (DSC) and Hausdorff Distance demonstrate that the proposed approach consistently outperforms classical active contour models and achieves competitive performance compared to learning-based methods, particularly in challenging cases involving low contrast and irregular lesion boundaries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The proposed fractional active contour model provides a robust, fully unsupervised, and interpretable solution for mammographic mass segmentation. By jointly exploiting adaptive fractional enhancement, Hessian-based curvature information, and local intensity fitting, the method effectively addresses key challenges inherent to mammogram images, making it a promising tool for assisting breast cancer detection in CAD systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmss.jmss_108_25","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:00:39Z","timestamp":1782129639000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Fractional Active Contour Model with Adaptive Hessian Weighting for Mammogram Segmentation"],"prefix":"10.4103","volume":"16","author":[{"given":"Ruhollah","family":"Motamedi","sequence":"first","affiliation":[{"name":"Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, 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