Detecting microcalcifications in digital mammograms using wavelet domain hidden markov tree model
In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov tree model (WHMT) for its inclusion to a computer-aided diagnostic prompting system for detecting microcalcification (MC) clusters. The system incorporates: (1) gross-segmentation of mammograms for obtaining the breast region; (2) eliminating the pepper-type noise, (3) block-wise wavelet transform of the breast signal and likelihood calculation; (4) image segmentation; (5) postprocessing for retaining MC clusters. FROC curves are obtained for all MC clusters containing mammograms of mini-MIAS database. 100% of true positive cases are detected by the system at 2.9 false positives per case.
Breast cancer; Cities and towns; Hidden Markov models; Image databases; Image segmentation; Tree graphs; USA Councils; Wavelet coefficients; Wavelet domain; Wavelet transforms
Bioimaging and Biomedical Optics | Biomechanics and Biotransport | Biomedical Devices and Instrumentation | Biomedical Engineering and Bioengineering | Diagnosis | Electrical and Computer Engineering | Electromagnetics and Photonics | Medicine and Health Sciences | Molecular, Cellular, and Tissue Engineering | Surgical Procedures, Operative
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Detecting microcalcifications in digital mammograms using wavelet domain hidden markov tree model.
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Institute of Electrical and Electronics Engineers.