Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).

[1]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[2]  C. D. HAAGENSEN,et al.  Diseases of the Breast , 1972 .

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  J L Kelsey,et al.  Breast cancer epidemiology. , 1988, Cancer research.

[6]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[7]  B. Garra,et al.  Improving the Distinction between Benign and Malignant Breast Lesions: The Value of Sonographic Texture Analysis , 1993 .

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

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

[10]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[11]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[12]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[13]  Tomás Lozano-Pérez,et al.  Image database retrieval with multiple-instance learning techniques , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[14]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[15]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[17]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

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

[19]  Heng-Da Cheng,et al.  Color image segmentation based on homogram thresholding and region merging , 2002, Pattern Recognit..

[20]  Bernhard Pfahringer,et al.  A Two-Level Learning Method for Generalized Multi-instance Problems , 2003, ECML.

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

[22]  H. D. Cheng,et al.  Fuzzy homogeneity and scale-space approach to color image segmentation , 2003, Pattern Recognit..

[23]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[24]  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.

[25]  Zhi-Hua Zhou.  Multi-Instance Learning : A Survey , 2004 .

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

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

[28]  Zhi-Hua Zhou,et al.  Multi-Instance Learning from Supervised View , 2006, Journal of Computer Science and Technology.

[29]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[30]  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.

[31]  Young-Seuk Park,et al.  Self-Organizing Map , 2008 .

[32]  Xianglong Tang,et al.  Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance. , 2009, Ultrasound in medicine & biology.

[33]  H. Chenga,et al.  Automated breast cancer detection and classification using ultrasound images A survey , 2009 .

[34]  James R. Foulds,et al.  A review of multi-instance learning assumptions , 2010, The Knowledge Engineering Review.

[35]  Jacques Ferlay,et al.  Global Burden of Breast Cancer , 2010 .

[36]  Xianglong Tang,et al.  Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images , 2010, Pattern Recognit..

[37]  A. Jemal,et al.  Cancer Statistics, 2010 , 2010, CA: a cancer journal for clinicians.

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  Xavier Bresson,et al.  Ultrasound Image Segmentation of the Fetal Abdomen: a Semi-Supervised Patch-Based Approach , 2012, MICCAI 2012.

[40]  Mohammad H. Poursaeidi,et al.  Robust support vector machines for multiple instance learning , 2012, Annals of Operations Research.