Ten lectures on wavelets
The world according to wavelets: the story of a mathematical technique in the making
The world according to wavelets: the story of a mathematical technique in the making
Image segmentation by fuzzy clustering: methods and issues
Handbook of medical imaging
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Automated image analysis techniques for digital mammography
Automated image analysis techniques for digital mammography
Better breast cancer detection
IEEE Spectrum
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Integrated Computer-Aided Engineering
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
A novel fuzzy segmentation approach for brain MRI
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
A target-based color space for sea target detection
Applied Intelligence
Weaver Ant Colony Optimization-Based Neural Network Learning for Mammogram Classification
International Journal of Swarm Intelligence Research
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Clusters of microcalcifications in a mammogram may be an early indication of breast cancer. Unfortunately, due to size, shape and limited contrast from surrounding normal tissue, microcalcifications can occasionally be hard to detect in computer-aided detection (CAD) systems. These CAD systems can also be slow compared to a radiologist's performance when reviewing film-screen mammography. The research described here investigates a rapid, multiresolution-based approach combined with wavelet analysis to provide an accurate segmentation of potential calcifications. An initial multiresolution approach to fuzzy c-means (FCM) segmentation is employed to rapidly distinguish medically significant tissues. Tissue areas chosen for high-resolution analysis are broken into multiple windows. Within each window, wavelet analysis is used to generate a contrast image, and a local FCM segmentation generates an estimate of local intensity. A simple two-rule fuzzy system then combines intensity and contrast information to derive fuzzy memberships of pixels in the high-contrast, bright pixel class. A double threshold is finally applied to this fuzzy membership to detect and segment calcifications. This sequence of steps is shown to approach detection rates of conventional classifier designs and may therefore be useful as a pre-processing module for these systems to improve speed. Results are reported for 25 images obtained from the Digital Database for Screening Mammography (DDSM).