RESEARCH ARTICLE


Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features



Yanni Su, Yuanyuan Wang*, Jing Jiao, Yi Guo
Department of Electronic Engineering, Fudan University, Shanghai 200433, China


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 2029
Abstract HTML Views: 1291
PDF Downloads: 267
Total Views/Downloads: 3587
Unique Statistics:

Full-Text HTML Views: 1003
Abstract HTML Views: 707
PDF Downloads: 196
Total Views/Downloads: 1906



© Su et al.; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Electronic Engineering, Fudan University, Shanghai 200433, China; Tel/Fax: +86 21 65642756; E-mail: yywang@fudan.edu.cn


Abstract

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

Keywords: Breast ultrasonic images, fully automatic, region of interest, Normalized Cut, Affinity Propagation clustering.