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This article is part of the supplement: The 2010 International Conference on Bioinformatics and Computational Biology (BIOCOMP 2010): Systems Biology

Open Access Research article

Mass segmentation using a combined method for cancer detection

Jun Liu1, Jianxun Chen1, Xiaoming Liu1, Lei Chun2*, Jinshan Tang1* and Youping Deng3

Author Affiliations

1 College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China

2 Key laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

3 Rush University Cancer Center, Rush University Medical Center, Chicago, Illinois, USA

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BMC Systems Biology 2011, 5(Suppl 3):S6  doi:10.1186/1752-0509-5-S3-S6

Published: 23 December 2011

Abstract

Background

Breast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.

Results

In this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.

Conclusions

The new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.