An image segmentation framework for extracting tumors from breast magnetic resonance images

Le Sun; Jinyuan He; Xiaoxia Yin; Yanchun Zhang; Jeon-Hor Chen; Tomas Kron; Min-Ying Su

doi:doi:10.1142/s1793545818500141

Journal of Innovative Optical Health Sciences, 2018, 11 (4): 1850014, Published Online: Oct. 6, 2018

An image segmentation framework for extracting tumors from breast magnetic resonance images

论文大纲

Le Sun ^1,*Jinyuan He ²Xiaoxia Yin ²Yanchun Zhang ²Jeon-Hor Chen ^3,4Tomas Kron ⁵Min-Ying Su ³

Author Affiliations

¹ School of Computer and Software, Nanjing University of Information Science and Technology, P. R. China

² Center for Applied Informatics Victoria University, Australia

³ Center for Functional Onco-Imaging of the Department of Radiological Sciences, University of California Irvine, USA

⁴ Department of Radiology E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

⁵ Peter MacCallum Cancer Centre, Australia

Breast lesion image segmentation MRI

Abstract

Magnetic resonance imaging (MRI) has been a prevalence technique for breast cancer diagnosis. Computer-aided detection and segmentation of lesions from MRIs plays a vital role for the MRIbased disease analysis. There are two main issues of the existing breast lesion segmentation techniques: requiring manual delineation of Regions of Interests (ROIs) as a step of initialization; and requiring a large amount of labeled images for model construction or parameter learning, while in real clinical or experimental settings, it is highly challenging to get su±cient labeled MRIs. To resolve these issues, this work proposes a semi-supervised method for breast tumor segmentation based on super voxel strategies. After image segmentation with advanced cluster techniques, we take a supervised learning step to classify the tumor and nontumor patches in order to automatically locate the tumor regions in an MRI. To obtain the optimal performance of tumor extraction, we take extensive experiments to learn parameters for tumor segmentation and classification, and design 225 classifiers corresponding to different parameter settings. We call the proposed method as Semi-supervised Tumor Segmentation (SSTS), and apply it to both mass and nonmass lesions. Experimental results show better performance of SSTS compared with five state-of-the-art methods.

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Le Sun, Jinyuan He, Xiaoxia Yin, Yanchun Zhang, Jeon-Hor Chen, Tomas Kron, Min-Ying Su. An image segmentation framework for extracting tumors from breast magnetic resonance images[J]. Journal of Innovative Optical Health Sciences, 2018, 11(4): 1850014.

An image segmentation framework for extracting tumors from breast magnetic resonance images

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