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Title: Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system

Abstract

In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.

Authors:
; ; ;  [1]
  1. Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154 (United States)
Publication Date:
OSTI Identifier:
20951508
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 34; Journal Issue: 6; Other Information: DOI: 10.1118/1.2733800; (c) 2007 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; ACCURACY; BIOMEDICAL RADIOGRAPHY; CLASSIFICATION; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; MARKOV PROCESS; NEOPLASMS

Citation Formats

Regentova, Emma, Lei, Zhang, Jun, Zheng, Veni, Gopalkrishna, Department of Computer Science, Queens College-City University of New York, 6530 Kissena Boulevard, Flushing, New York 11367, and Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system. United States: N. p., 2007. Web. doi:10.1118/1.2733800.
Regentova, Emma, Lei, Zhang, Jun, Zheng, Veni, Gopalkrishna, Department of Computer Science, Queens College-City University of New York, 6530 Kissena Boulevard, Flushing, New York 11367, & Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system. United States. doi:10.1118/1.2733800.
Regentova, Emma, Lei, Zhang, Jun, Zheng, Veni, Gopalkrishna, Department of Computer Science, Queens College-City University of New York, 6530 Kissena Boulevard, Flushing, New York 11367, and Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154. Fri . "Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system". United States. doi:10.1118/1.2733800.
@article{osti_20951508,
title = {Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system},
author = {Regentova, Emma and Lei, Zhang and Jun, Zheng and Veni, Gopalkrishna and Department of Computer Science, Queens College-City University of New York, 6530 Kissena Boulevard, Flushing, New York 11367 and Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154},
abstractNote = {In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.},
doi = {10.1118/1.2733800},
journal = {Medical Physics},
issn = {0094-2405},
number = 6,
volume = 34,
place = {United States},
year = {2007},
month = {6}
}