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Microcalcification detection based on localized texture comparison
- Source :
- ICIP
- Publication Year :
- 2005
- Publisher :
- IEEE, 2005.
-
Abstract
- While microcalcifications (MCs) are important early signs of breast cancers, their reliable detection from mammograms has been largely elusive for both radiologists and computer-aided diagnosis (CAD) strategies. Two of the essential components in a CAD system are the detection of the suspicious MC pixels/regions using image processing and analysis techniques, and the training, classification, and recognition of these areas based on pattern recognition methods. In this paper, we present a novel scheme to identify and classify microcalcifications based on localized texture comparison. Relying on a texture removal and repairing (R&R) process of the preselected suspicious areas from their surrounding background tissues, pre- and post- R&R local characteristic features of these areas are extracted and compared. A modified AdaBoost algorithm is then adopted to train the classifier using expert-labelled microcalcifications, followed by a clustering process. Experiments with the mammographic images from the MIAS and DDSM databases have shown very promising results.
- Subjects :
- Pixel
Contextual image classification
medicine.diagnostic_test
Computer science
business.industry
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cancer
Pattern recognition
Image processing
medicine.disease
ComputingMethodologies_PATTERNRECOGNITION
Image texture
medicine
Mammography
Computer vision
Microcalcification
Artificial intelligence
medicine.symptom
Cluster analysis
business
Image resolution
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2004 International Conference on Image Processing, 2004. ICIP '04.
- Accession number :
- edsair.doi...........a560564aaccf6861e2ed434c2053a63d