Computer-aided diagnosis with textural features for breast lesions in sonograms

Abstract Rationale and objectives Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. Materials...

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Bibliographic Details
Published in:Computerized medical imaging and graphics 2011-04, Vol.35 (3), p.220-226
Main Authors: Chen, Dar-Ren, Huang, Yu-Len, Lin, Sheng-Hsiung
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Breast cancer
Breast Neoplasms - diagnostic imaging
Computer-aided diagnosis
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image retrieval
Internal Medicine
Middle Aged
Other
Pattern Recognition, Automated - methods
Principal component analysis
Reproducibility of Results
Sensitivity and Specificity
Texture analysis
Ultrasonography, Mammary - methods
Ultrasound
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Summary:Abstract Rationale and objectives Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. Materials and methods The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k -fold cross-validation ( k = 10) to evaluate the performance with receiver operating characteristic (ROC) curve. Results The area ( A Z ) under the ROC curve for the proposed CAD system with the specific textural features was 0.925 ± 0.019. The classification ability for breast tumor with textural information is satisfactory. Conclusions This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion.
ISSN:0895-6111
1879-0771
DOI:10.1016/j.compmedimag.2010.11.003