Yuan Feng1,2, Hao Guo1,2, Hongmiao Zhang1,2, Chungang Li1,2, Lining Sun1,2, Sasa Mutic3, Songbai Ji4, Yanle Hu3,5. 1. School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China. 2. Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China. 3. Department of Radiation Oncology, Washington University, St. Louis, MO, USA. 4. Thayer School of Engineering, Dartmouth College, Hanover, NH, USA. 5. Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
Abstract
BACKGROUND: In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation. OBJECTIVE: The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise. METHODS: A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared. RESULTS: Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region. CONCLUSIONS: The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).
BACKGROUND: In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation. OBJECTIVE: The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise. METHODS: A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared. RESULTS: Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region. CONCLUSIONS: The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).
Authors: Derik L Davis; Mohit N Gilotra; Rodolfo Calderon; Andrew Roberts; S Ashfaq Hasan Journal: Skeletal Radiol Date: 2021-05-06 Impact factor: 2.199
Authors: Derik L Davis; Andrew Roberts; Rodolfo Calderon; Shihyun Kim; Alice S Ryan; Tatiana V D Sanses Journal: Skeletal Radiol Date: 2022-07-27 Impact factor: 2.128