Title :
Detecting Microcalcifications in Digital Mammograms using Wavelet Domain Hidden Markov Tree Model
Author :
Regentova, Emma ; Zhang, Lei ; Zheng, Jun ; Veni, Gopalkrishna
Author_Institution :
Dept. of Electr. & Comput. Eng., Nevada Univ., Las Vegas, NV
fDate :
Aug. 30 2006-Sept. 3 2006
Abstract :
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
Keywords :
cancer; diagnostic radiography; hidden Markov models; image denoising; image segmentation; mammography; medical image processing; tumours; wavelet transforms; FROC curves; block-wise wavelet transform; breast region; cancer; computer-aided diagnostic system; digital mammograms; gross-segmentation; image segmentation; likelihood calculation; microcalcification detection; miniMIAS database; pepper-type noise elimination; statistical modeling; wavelet domain hidden Markov tree model; Breast cancer; Cities and towns; Hidden Markov models; Image databases; Image segmentation; Tree graphs; USA Councils; Wavelet coefficients; Wavelet domain; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.259580
