Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation

• Published in: MethodsX Vol. 14; p. 103224
• Main Authors: Kalpana, G., Deepa, N., Dhinakaran, D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2025; Elsevier
• Subjects: AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network); Augmentation; Breast cancer; breast neoplasms; Computer Science; Equalization; image analysis; Multi-scale region enhancement; neural networks; Normalization; pipelines; Segmentation; solutions; ultrasonics; Augmentation; AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network); Segmentation; Breast cancer; Normalization; Equalization; Multi-scale region enhancement
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2025.103224

The segmentation of breast cancer diagnosis and medical imaging contains issues such as noise, variation in contrast, and low resolutions which make it challenging to distinguish malignant sites. In this paper, we propose a new solution that integrates with AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network) to overcome these problems. The preprocessing pipeline apply bunch of methods including Adaptive Thresholding, Hierarchical Contrast Normalization, Contextual Feature Augmentation, Multi-Scale Region Enhancement, and Dynamic Histogram Equalization for image quality. These methods smooth edges, equalize the contrasting picture and inlay contextual details in a way which effectively eliminate the noise and make the images clearer and with fewer distortions. Experimental outcomes demonstrate its effectiveness by delivering a Dice Coefficient of 0.89, IoU of 0.85, and a Hausdorff Distance of 5.2 demonstrating its enhanced capability in segmenting significant tumor margins over other techniques. Furthermore, the use of the improved preprocessing pipeline benefits classification models with improved Convolutional Neural Networks having a classification accuracy of 85.3 % coupled with AUC-ROC of 0.90 which shows a significant enhancement from conventional techniques.•Enhanced segmentation accuracy with advanced preprocessing and CASDN, achieving superior performance metrics.•Robust multi-modality compatibility, ensuring effectiveness across mammograms, ultrasounds, and MRI scans. [Display omitted]