skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis

Abstract

A computerized regional registration and characterization system for analysis of microcalcification clusters on serial mammograms is being developed in our laboratory. The system consists of two stages. In the first stage, based on the location of a detected cluster on the current mammogram, a regional registration procedure identifies the local area on the prior that may contain the corresponding cluster. A search program is used to detect cluster candidates within the local area. The detected cluster on the current image is then paired with the cluster candidates on the prior image to form true (TP-TP) or false (TP-FP) pairs. Automatically extracted features were used in a newly designed correspondence classifier to reduce the number of false pairs. In the second stage, a temporal classifier, based on both current and prior information, is used if a cluster has been detected on the prior image, and a current classifier, based on current information alone, is used if no prior cluster has been detected. The data set used in this study consisted of 261 serial pairs containing biopsy-proven calcification clusters. An MQSA radiologist identified the corresponding clusters on the mammograms. On the priors, the radiologist rated the subtlety of 30 clusters (out ofmore » the 261 clusters) as 9 or 10 on a scale of 1 (very obvious) to 10 (very subtle). Leave-one-case-out resampling was used for feature selection and classification in both the correspondence and malignant/benign classification schemes. The search program detected 91.2%(238/261) of the clusters on the priors with an average of 0.42 FPs/image. The correspondence classifier identified 86.6%(226/261) of the TP-TP pairs with 20 false matches (0.08 FPs/image) relative to the entire set of 261 image pairs. In the malignant/benign classification stage the temporal classifier achieved a test A{sub z} of 0.81 for the 246 pairs which contained a detection on the prior. In addition, a classifier was designed by using the clusters on the current mammograms only. It achieved a test A{sub z} of 0.72 in classifying the clusters as malignant and benign. The difference between the performance of the temporal classifier and the current classifier was statistically significant (p=0.0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy.« less

Authors:
; ; ; ; ; ; ;  [1]
  1. Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-0904 (United States)
Publication Date:
OSTI Identifier:
22095273
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 35; Journal Issue: 12; Other Information: (c) 2008 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; 97 MATHEMATICAL METHODS AND COMPUTING; ACCURACY; BIOMEDICAL RADIOGRAPHY; BIOPSY; CLASSIFICATION; DIAGNOSIS; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; NEOPLASMS

Citation Formats

Filev, Peter, Hadjiiski, Lubomir, Chan, Heang-Ping, Sahiner, Berkman, Jun, Ge, Helvie, Mark A., Roubidoux, Marilyn, and Chuan, Zhou. Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis. United States: N. p., 2008. Web. doi:10.1118/1.3002311.
Filev, Peter, Hadjiiski, Lubomir, Chan, Heang-Ping, Sahiner, Berkman, Jun, Ge, Helvie, Mark A., Roubidoux, Marilyn, & Chuan, Zhou. Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis. United States. doi:10.1118/1.3002311.
Filev, Peter, Hadjiiski, Lubomir, Chan, Heang-Ping, Sahiner, Berkman, Jun, Ge, Helvie, Mark A., Roubidoux, Marilyn, and Chuan, Zhou. Mon . "Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis". United States. doi:10.1118/1.3002311.
@article{osti_22095273,
title = {Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis},
author = {Filev, Peter and Hadjiiski, Lubomir and Chan, Heang-Ping and Sahiner, Berkman and Jun, Ge and Helvie, Mark A. and Roubidoux, Marilyn and Chuan, Zhou},
abstractNote = {A computerized regional registration and characterization system for analysis of microcalcification clusters on serial mammograms is being developed in our laboratory. The system consists of two stages. In the first stage, based on the location of a detected cluster on the current mammogram, a regional registration procedure identifies the local area on the prior that may contain the corresponding cluster. A search program is used to detect cluster candidates within the local area. The detected cluster on the current image is then paired with the cluster candidates on the prior image to form true (TP-TP) or false (TP-FP) pairs. Automatically extracted features were used in a newly designed correspondence classifier to reduce the number of false pairs. In the second stage, a temporal classifier, based on both current and prior information, is used if a cluster has been detected on the prior image, and a current classifier, based on current information alone, is used if no prior cluster has been detected. The data set used in this study consisted of 261 serial pairs containing biopsy-proven calcification clusters. An MQSA radiologist identified the corresponding clusters on the mammograms. On the priors, the radiologist rated the subtlety of 30 clusters (out of the 261 clusters) as 9 or 10 on a scale of 1 (very obvious) to 10 (very subtle). Leave-one-case-out resampling was used for feature selection and classification in both the correspondence and malignant/benign classification schemes. The search program detected 91.2%(238/261) of the clusters on the priors with an average of 0.42 FPs/image. The correspondence classifier identified 86.6%(226/261) of the TP-TP pairs with 20 false matches (0.08 FPs/image) relative to the entire set of 261 image pairs. In the malignant/benign classification stage the temporal classifier achieved a test A{sub z} of 0.81 for the 246 pairs which contained a detection on the prior. In addition, a classifier was designed by using the clusters on the current mammograms only. It achieved a test A{sub z} of 0.72 in classifying the clusters as malignant and benign. The difference between the performance of the temporal classifier and the current classifier was statistically significant (p=0.0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy.},
doi = {10.1118/1.3002311},
journal = {Medical Physics},
issn = {0094-2405},
number = 12,
volume = 35,
place = {United States},
year = {2008},
month = {12}
}