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

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.

[1]  Ruey-Feng Chang,et al.  Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. , 2002, Ultrasound in medicine & biology.

[2]  D. Chen,et al.  Breast cancer diagnosis using self-organizing map for sonography. , 2000, Ultrasound in medicine & biology.

[3]  A. Stavros,et al.  Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. , 1995, Radiology.

[4]  R. Chang,et al.  Retrieval technique for the diagnosis of solid breast tumors on sonogram. , 2002, Ultrasound in medicine & biology.

[5]  Nam Chul Kim,et al.  Image retrieval using BDIP and BVLC moments , 2003, IEEE Trans. Circuits Syst. Video Technol..

[6]  Vijay V. Raghavan,et al.  Content-Based Image Retrieval Systems - Guest Editors' Introduction , 1995, Computer.

[7]  S. Kuo,et al.  Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems , 2005, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[8]  Constantinos S. Pattichis,et al.  Texture-based classification of atherosclerotic carotid plaques , 2003, IEEE Transactions on Medical Imaging.

[9]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[11]  Georgy L. Gimel'farb,et al.  On retrieving textured images from an image database , 1996, Pattern Recognit..

[12]  Ruey-Feng Chang,et al.  Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis. , 2003, Ultrasound in medicine & biology.

[13]  D. Chen,et al.  Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. , 1999, Radiology.

[14]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[15]  Venkat N. Gudivada,et al.  An Architecture for and Query Processing in Distributed Content-based Image Retrieval , 1996, Real Time Imaging.

[16]  Sholom M. Weiss,et al.  An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[17]  D. Chen,et al.  Texture analysis of breast tumors on sonograms. , 2000, Seminars in ultrasound, CT, and MR.

[18]  Simone G. O. Fiori,et al.  Image compression using principal component neural networks , 2001, Image Vis. Comput..

[19]  Ruey-Feng Chang,et al.  3-D ultrasound texture classification using run difference matrix. , 2005, Ultrasound in medicine & biology.

[20]  Usha Sinha,et al.  Principal component analysis for content-based image retrieval. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.

[21]  S C Horii,et al.  Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. , 1993, Ultrasonic imaging.