Improving classification performance of breast lesions on ultrasonography

Several morphological and texture features aiming to distinguish between benign and malignant lesions on breast ultrasound (BUS) have been proposed in the literature. Various authors also claim that their particular feature sets are capable of reaching adequate classification rate. However, there are still several features that have not been tested together for determining the feature set that effectively improves classification performance. Hence, in this paper, we compiled distinct morphological and texture features widely used in computer-aided diagnosis systems for BUS images. A total of 26 morphological and 1465 texture features were computed from 641 BUS images (413 benign and 228 malignant lesions). A feature selection methodology, based on mutual information and statistical tests, was used to evaluate the discrimination power of distinct feature subsets. The .632+ bootstrap method was used to estimate the classification performance of each feature subset, by using the local Fisher discriminant analysis (LFDA), with linear kernel, as classifier, and the area under ROC curve (AUC) as performance index. The experimental results indicated that the best classification performance is AUC=0.942, obtained by a morphological set with five features. In addition, this morphological set outperformed the best texture set with four features, which attained AUC=0.897. The classification performances of 11 feature sets proposed in the literature were also surpassed by such morphological feature set. Highlights1491 features are evaluated for classifying breast lesions on ultrasound.Feature selection is based on mutual information and statistical tests.5 morphological and 4 texture features achieve the best classification performance.11 feature sets from the literature are surpassed by the 5 morphological features.

[1]  Helmut Ermert,et al.  A Tutorial on the Use of ROC Analysis for Computer-Aided Diagnostic Systems , 2005, Ultrasonic imaging.

[2]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

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

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

[5]  R. Chang,et al.  Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. , 2003, Ultrasound in medicine & biology.

[6]  W C A Pereira,et al.  Intraobserver interpretation of breast ultrasonography following the BI-RADS classification. , 2010, European journal of radiology.

[7]  Ruey-Feng Chang,et al.  Classification of breast ultrasound images using fractal feature. , 2005, Clinical imaging.

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  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.

[10]  Jeon-Hor Chen,et al.  Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis , 2013, IEEE Transactions on Medical Imaging.

[11]  Lubomir M. Hadjiiski,et al.  Classifier performance prediction for computer-aided diagnosis using a limited dataset. , 2008, Medical physics.

[12]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[13]  L Leija,et al.  Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation. , 2009, Medical physics.

[14]  W. Pereira,et al.  Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images. , 2010, Medical engineering & physics.

[15]  W. Moon,et al.  Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[16]  M K Markey,et al.  Application of the mutual information criterion for feature selection in computer-aided diagnosis. , 2001, Medical physics.

[17]  Wagner Coelho A. Pereira,et al.  Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound , 2012, IEEE Transactions on Medical Imaging.

[18]  Enzo Galligioni,et al.  Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1 year follow-up. , 2011, European journal of cancer.

[19]  Ruey-Feng Chang,et al.  Breast ultrasound computer-aided diagnosis using BI-RADS features. , 2007, Academic radiology.

[20]  J W Sayre,et al.  Benign versus malignant solid breast masses: US differentiation. , 1999, Radiology.

[21]  Jean B. Cormack,et al.  Operator dependence of physician-performed whole-breast US: lesion detection and characterization. , 2006, Radiology.

[22]  Brijesh Verma,et al.  Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection , 2005, Pattern Recognit. Lett..

[23]  Annette M. Molinaro,et al.  Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..

[24]  Ling Zhang,et al.  Automated breast cancer detection and classification using ultrasound images: A survey , 2015, Pattern Recognit..

[25]  M. R. Stoline.  The Status of Multiple Comparisons: Simultaneous Estimation of all Pairwise Comparisons in One-Way ANOVA Designs , 1981 .

[26]  Isabelle Borget,et al.  Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist's experience. , 2012, Academic radiology.

[27]  Wagner Coelho A. Pereira,et al.  A non-linear morphometric feature selection approach for breast tumor contour from ultrasonic images , 2010, Comput. Biol. Medicine.

[28]  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.

[29]  Ruey-Feng Chang,et al.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors , 2004, Breast Cancer Research and Treatment.

[30]  R. Tibshirani,et al.  Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .

[31]  Yi Guo,et al.  Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features , 2011, The open medical informatics journal.

[32]  W. Pereira,et al.  Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images. , 2012, Medical physics.

[33]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

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

[35]  M J M Broeders,et al.  A dedicated BI-RADS training programme: effect on the inter-observer variation among screening radiologists. , 2012, European journal of radiology.

[36]  André Victor Alvarenga,et al.  Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images. , 2007, Medical physics.

[37]  D. ChengH.,et al.  Automated breast cancer detection and classification using ultrasound images , 2010 .

[38]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

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

[40]  Sergios Theodoridis,et al.  Pattern Recognition, Third Edition , 2006 .

[41]  Athina P. Petropulu,et al.  ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis , 2003, IEEE Transactions on Medical Imaging.

[42]  M. Giger,et al.  Breast US computer-aided diagnosis workstation: performance with a large clinical diagnostic population. , 2008, Radiology.