Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound

Objectives To develop and evaluate a computer‐aided diagnosis (CAD) system with automatic contouring and morphological analysis to aid in the classification of breast tumors using ultrasound. Methods We evaluated 118 breast lesions (34 malignant and 84 benign tumors). Each tumor contour was automati...

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Bibliographic Details
Published in:Ultrasound in obstetrics & gynecology 2008-09, Vol.32 (4), p.565-572
Main Authors: Huang, Y.‐L., Chen, D.‐R., Jiang, Y.‐R., Kuo, S.‐J., Wu, H.‐K., Moon, W. K.
Format: Article
Language:English
Subjects:
Adult
Aged
Algorithms
Biological and medical sciences
breast cancer
Breast Neoplasms - diagnostic imaging
breast sonography
computer‐aided diagnosis
Diagnosis, Computer-Assisted - methods
Diagnosis, Differential
Female
Gynecology. Andrology. Obstetrics
Humans
Image Interpretation, Computer-Assisted - methods
Mammary gland diseases
Medical sciences
Middle Aged
morphological analysis
Principal Component Analysis
support vector machine
tumor classification
tumor segmentation
Tumors
Ultrasonography, Mammary - methods
Young Adult
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Summary:Objectives To develop and evaluate a computer‐aided diagnosis (CAD) system with automatic contouring and morphological analysis to aid in the classification of breast tumors using ultrasound. Methods We evaluated 118 breast lesions (34 malignant and 84 benign tumors). Each tumor contour was automatically extracted from the digitized ultrasound image. Nineteen practical morphological features from the extracted contour were calculated and principal component analysis (PCA) was applied to find independent features. A support vector machine (SVM) classifier utilized the selected principal vectors to identify the breast tumor as benign or malignant. In this study, all the cases were sampled with k‐fold cross‐validation (k = 10) to evaluate the performance by receiver–operating characteristics (ROC) curve analysis. Results The areas under the ROC curves for the proposed CAD systems using all morphological features and the lower‐dimensional principal vector were 0.91 and 0.90, respectively. The classification ability for breast tumors using morphological information was good. Conclusions This system differentiates benign from malignant breast tumors well and therefore provides a clinically useful second opinion. Moreover, the morphological features are nearly setting‐independent and thus available to various ultrasound machines. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.
ISSN:0960-7692
1469-0705
DOI:10.1002/uog.5205