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Title: Characterization of mammographic masses based on level set segmentation with new image features and patient information

Abstract

Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD systemmore » based on the level set segmentation and the new mammographic feature space achieved a view-based A{sub z} value of 0.83{+-}0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based A{sub z} values were 0.85{+-}0.01 and 0.87{+-}0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening mammography with 132 benign and 197 malignant ROIs containing masses achieved a view-based A{sub z} value of 0.84{+-}0.02.« less

Authors:
; ; ; ; ; ; ; ; ; ; ;  [1]
  1. Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-0904 (United States)
Publication Date:
OSTI Identifier:
21036134
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 35; Journal Issue: 1; Other Information: DOI: 10.1118/1.2820630; (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; ACCURACY; BIOMEDICAL RADIOGRAPHY; BIOPSY; CLASSIFICATION; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; PATIENTS

Citation Formats

Jiazheng, Shi, Sahiner, Berkman, Heangping, Chan, Jun, Ge, Hadjiiski, Lubomir, Helvie, Mark A, Nees, Alexis, Yita, Wu, Jun, Wei, Chuan, Zhou, Yiheng, Zhang, and Jing, Cui. Characterization of mammographic masses based on level set segmentation with new image features and patient information. United States: N. p., 2008. Web. doi:10.1118/1.2820630.
Jiazheng, Shi, Sahiner, Berkman, Heangping, Chan, Jun, Ge, Hadjiiski, Lubomir, Helvie, Mark A, Nees, Alexis, Yita, Wu, Jun, Wei, Chuan, Zhou, Yiheng, Zhang, & Jing, Cui. Characterization of mammographic masses based on level set segmentation with new image features and patient information. United States. https://doi.org/10.1118/1.2820630
Jiazheng, Shi, Sahiner, Berkman, Heangping, Chan, Jun, Ge, Hadjiiski, Lubomir, Helvie, Mark A, Nees, Alexis, Yita, Wu, Jun, Wei, Chuan, Zhou, Yiheng, Zhang, and Jing, Cui. Tue . "Characterization of mammographic masses based on level set segmentation with new image features and patient information". United States. https://doi.org/10.1118/1.2820630.
@article{osti_21036134,
title = {Characterization of mammographic masses based on level set segmentation with new image features and patient information},
author = {Jiazheng, Shi and Sahiner, Berkman and Heangping, Chan and Jun, Ge and Hadjiiski, Lubomir and Helvie, Mark A and Nees, Alexis and Yita, Wu and Jun, Wei and Chuan, Zhou and Yiheng, Zhang and Jing, Cui},
abstractNote = {Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based A{sub z} value of 0.83{+-}0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based A{sub z} values were 0.85{+-}0.01 and 0.87{+-}0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening mammography with 132 benign and 197 malignant ROIs containing masses achieved a view-based A{sub z} value of 0.84{+-}0.02.},
doi = {10.1118/1.2820630},
url = {https://www.osti.gov/biblio/21036134}, journal = {Medical Physics},
issn = {0094-2405},
number = 1,
volume = 35,
place = {United States},
year = {2008},
month = {1}
}