Three-Dimensional Segmentation for Fibroglandular Tissues on Breast MRI

10.29428/9789860544169.201801.0074

Guan-Ze Wu；Dar-Ren Chen；Yi-Chun Chen；Yu-Len Huang

breast cancer ； magnetic resonance imaging ； breast volume ； breast density ； image segmentation ； 3D region growing

NCS 2017 全國計算機會議

2017（2018 / 01 / 01）

389 - 392

英文

Breast cancer is the most common cancer in woman. The development and progress of medical research, if early detection and treatment can improve the cure rate of breast cancer. There are many ways to diagnose breast tumors in medical imaging tools, such as mammography, ultrasonography and magnetic resonance imaging (MRI). In computer aided analysis of MRI, contouring of breast fibroglandular region is an important step. Accurate volume of fibroglandular tissue and breast density should help physicians to effective predict the risk of cancer. As breast MRI becomes more widespread used, a functional automatic method for extracting fibroglandular breast tissue is essential and its clinical application is becoming urgent. This study proposes a robust segmentation method to assist the physician on contouring breast fibroglandular boundary. The proposed method first utilizes the anisotropic diffusion filtering to reduce the noises and speckle in MRI images. Three-dimensional (3D) region growing method is applied to segment the breast fibroglandular area. Finally, the proposed method made the area smoother and correctly though a post processing step. All segmentation methods are three-dimensional, compared to two-dimensional segmentation can be considered more relevance, the results more accurate.