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-s...

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Veröffentlicht in:	2006 International Conference of the IEEE Engineering in Medicine and Biology Society 2006, Vol.2006, p.1972-1975
Hauptverfasser:	Regentova, E., Zhang, L., Zheng, J., Veni, G.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Breast cancer Breast Neoplasms - diagnostic imaging Calcinosis - diagnostic imaging Cities and towns Computer Simulation Female Hidden Markov models Humans Image databases Image segmentation Information Storage and Retrieval - methods Mammography - methods Markov Chains Models, Biological Models, Statistical Pattern Recognition, Automated - methods Precancerous Conditions - diagnostic imaging Radiographic Image Enhancement - methods Radiographic Image Interpretation, Computer-Assisted - methods Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Tree graphs USA Councils Wavelet coefficients Wavelet domain Wavelet transforms
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Zusammenfassung:	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
ISSN:	1557-170X
DOI:	10.1109/IEMBS.2006.259580