Multiview Contouring for Breast Tumor on Magnetic Resonance Imaging
- 11 Downloads
The shape and contour of the lesion are shown to be effective features for physicians to identify breast tumor as benign or malignant. The region of the lesion is usually manually created by the physician according to their clinical experience; therefore, contouring tumors on breast magnetic resonance imaging (MRI) is difficult and time-consuming. For this purpose, an automatic contouring method for breast tumors was developed for less burden in the analysis and to decrease the observed bias to help in making decisions clinically. In this study, a multiview segmentation method for detecting and contouring breast tumors in MRI was represented. The preprocessing of the proposed method reduces any amount of noises but preserves the shape and contrast of the breast tumor. The two-dimensional (2D) level-set segmentation method extracts contours of breast tumors from the transverse, coronal, and sagittal planes. The obtained contours are further utilized to generate appropriate three-dimensional (3D) contours. Twenty breast tumor cases were evaluated and the simulation results show that the proposed contouring method was an efficient method for delineating 3D contours of breast tumors in MRI.
KeywordsBreast cancer MRI Image segmentation Level-set method Multiview contouring
The authors would like to thank the Ministry of Science and Technology, Taiwan, for financially supporting this research under Contract No. MOST 106-2221-E-029-029.
- 1.ACS: Breast Cancer Facts and Figures 2017-2018. Atlanta: American Cancer Society, 2017Google Scholar
- 5.Park VY, Kim EK, Kim MJ, Moon HJ, Yoon JH: Breast magnetic resonance imaging for surveillance of women with a personal history of breast cancer: outcomes stratified by interval between definitive surgery and surveillance MR imaging. BMC Cancer 18(91):91, 2018CrossRefPubMedPubMedCentralGoogle Scholar
- 12.Woods RCGaRE: Digital Image Processing 3nd Edition. Chapter 104 Region Growing Segmentation: 763–769, 2008Google Scholar
- 16.Wang C-M, Huang C-L, Yang S-C: 3D shape-weighted level set method for breast MRI 3D tumor segmentation. Journal of Healthcare Engineering 2018(15):1–15, 2018Google Scholar
- 19.Woods RCGaRE: Digital Image Processing 3rd edition. Chapter 58 Minimum Mean Square Error (Wiener) Filtering:352–357, 2008Google Scholar