Fractional Active Contour Model with Adaptive Hessian Weighting for Mammogram Segmentation.

Authors

Ruhollah Motamedi, Nasser Aghazadeh, Mahdi Hashemzadeh, Parisa Noras

Published in

Journal of medical signals and sensors. Volume 16. Pages 17. Epub Jul 01, 2026.

Abstract

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.
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.
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.
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.

PMID:
42434353
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.