Improving Classification Performance of Spatial Filters in Mammographic Microcalcifications Images Using Persistent Homology

Noise and artefacts in mammogram images can obscure important indicators of microcalcifications, complicating accurate diagnosis. While traditional spatial filters can reduce noise and are effective to some extent, they often fail to enhance features crucial for classification. This study uses persistent homology (PH) to evaluate and improve the classification performance of various spatial filters on mammogram images. The evaluation process involves converting filtered images into persistence diagrams (PDs) to capture topological features. These diagrams are then vectorised into PH features for classification using a neural network classifier. This study also examines further filtering of PDs from filtered images to enhance classification performance. Using the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets, we evaluate Median, Wiener, Gaussian, and Bilateral filters alone and integrate them with PH-based filtering. Results show significant classification improvements, with Wiener filters achieving 96.33% accuracy on the DDSM dataset (up from 57.38%) and Gaussian filters reaching 85.33% on the MIAS dataset (up from 73.33%). These findings demonstrate the potential of PH-based filters to enhance diagnostic accuracy in breast cancer detection by refining topological features and effectively reducing noise.