Measuring shape complexity of breast lesions on ultrasound images

The shapes of malignant breast tumors are more complex than the benign lesions due to their nature of infiltration into surrounding tissues. We investigated the efficacy of shape features and presented a method using polygon shape complexity to improve the discrimination of benign and malignant breast lesions on ultrasound. First, 63 lesions (32 benign and 31 malignant) were segmented by K-way normalized cut with the priori rules on the ultrasound images. Then, the shape measures were computed from the automatically extracted lesion contours. A polygon shape complexity measure (SCM) was introduced to characterize the complexity of breast lesion contour, which was calculated from the polygonal model of lesion contour. Three new statistical parameters were derived from the local integral invariant signatures to quantify the local property of the lesion contour. Receiver operating characteristic (ROC) analysis was carried on to evaluate the performance of each individual shape feature. SCM outperformed the other shape measures, the area under ROC curve (AUC) of SCM was 0.91, and the sensitivity of SCM could reach 0.97 with the specificity 0.66. The measures of shape feature and margin feature were combined in a linear discriminant classifier. The resubstitution and leave-one-out AUC of the linear discriminant classifier were 0.94 and 0.92, respectively. The distinguishing ability of SCM showed that it could be a useful index for the clinical diagnosis and computer-aided diagnosis to reduce the number of unnecessary biopsies.

[1]  K. Han,et al.  Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. , 2003, Radiology.

[2]  Wen-Yen Wu,et al.  An adaptive method for detecting dominant points , 2003, Pattern Recognit..

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

[4]  Tsuyoshi Shiina,et al.  Quantitative evaluation of diagnostic information around the contours in ultrasound images , 2005, Journal of Medical Ultrasonics.

[5]  Maryellen L Giger,et al.  Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. , 2004, Academic radiology.

[6]  R. Rangayyan,et al.  Boundary modelling and shape analysis methods for classification of mammographic masses , 2000, Medical and Biological Engineering and Computing.

[7]  E. Conant,et al.  A Review of Breast Ultrasound , 2006, Journal of Mammary Gland Biology and Neoplasia.

[8]  Hongtao Lu,et al.  Automatic Feature Extraction and Analysis on Breast Ultrasound Images , 2007, ICIC.

[9]  Jianbo Shi,et al.  Multiclass spectral clustering , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Susan M. Schultz,et al.  Computer‐Based Margin Analysis of Breast Sonography for Differentiating Malignant and Benign Masses , 2004, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[11]  C M Sehgal,et al.  Diffuse boundary extraction of breast masses on ultrasound by leak plugging. , 2005, Medical physics.

[12]  Jianbo Shi,et al.  Spectral segmentation with multiscale graph decomposition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Rangaraj M. Rangayyan,et al.  Feature Extraction from a Signature Based on the Turning Angle Function for the Classification of Breast Tumors , 2008, Journal of Digital Imaging.

[14]  D Cavouras,et al.  Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[15]  Jong Hyo Kim,et al.  Computerized scheme for assessing ultrasonographic features of breast masses. , 2005, Academic radiology.

[16]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[17]  Daniel Cremers,et al.  Integral Invariants for Shape Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Stefano Soatto,et al.  Integral Invariant Signatures , 2004, ECCV.

[19]  Dar-Ren Chen,et al.  Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines , 2006, Neural Computing & Applications.

[20]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Radhika Sivaramakrishna,et al.  Texture analysis of lesions in breast ultrasound images. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[22]  Rangaraj M. Rangayyan,et al.  Spiculation-preserving Polygonal Modeling of Contours of Breast Tumors , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Dimitris N. Metaxas,et al.  Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions , 2003, IEEE Transactions on Medical Imaging.

[24]  R. Birdwell.  BI-RADS Lexicon for US and Mammography: Interobserver Variability and Positive Predictive ValueLazarus E, Mainiero MB, Schepps B, et al (Brown Med School, Providence, RI) Radiology 239:385–391, 2006§ , 2007 .

[25]  M. Giger,et al.  Computerized diagnosis of breast lesions on ultrasound. , 2002, Medical physics.

[26]  Stanley Osher,et al.  Image Denoising and Decomposition with Total Variation Minimization and Oscillatory Functions , 2004, Journal of Mathematical Imaging and Vision.

[27]  Zhimin Huo,et al.  Automated segmentation of breast lesions in ultrasound images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[28]  Serge J. Belongie,et al.  Normalized cuts in 3-D for spinal MRI segmentation , 2004, IEEE Transactions on Medical Imaging.

[29]  Hee Chan Kim,et al.  Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features , 2004, IEEE Trans. Medical Imaging.

[30]  Hans-Peter Kriegel,et al.  Measuring the Complexity of Polygonal Objects , 1995, ACM-GIS.

[31]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[32]  Y. Chou,et al.  Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. , 2001, Ultrasound in Medicine and Biology.

[33]  M. Mainiero,et al.  BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value. , 2006, Radiology.

[34]  Stefano Soatto,et al.  Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[35]  Sung-Nien Yu,et al.  Quantitatively Characterizing the Textural Features of Sonographic Images for Breast Cancer With Histopathologic Correlation , 2005, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.