Earticle

초록

영어

Errors in the scanning procedures lead to uncertainties when trying to segment the scanned images. Fuzzy c-means is a clustering method that can be applied to segment images with uncertainty estimates. Bias-corrected fuzzy c-means (BCFCM) clustering compensates for two sources of uncertainty by modeling noise and bias fields during the segmentation process. In this paper, we present an approach to improve BCFCM clustering and apply it to magnetic resonance imaging (MRI) data of the human brain. Our approach is based on two variants of BCFCM clustering, the classical one and the one with distance-based weights. We improve both variants by slightly modifying their main algorithms for better bias field estimation. To evaluate the improved algorithms, we apply the algorithms to synthetic data, simulated MRI brain data, and real MRI brain data with ground truth in form of manual segmentation. All experiment results show that our improved methods outperform the original methods in both the segmentation accuracy and efficiency (the number of iterations).

목차

Abstract
 1. Introduction
 2. Related Work
 3. FCM Methods
  3.1. Standard FCM Approach
  3.2. BCFCM Approach
  3.3. BCFCM_WA Approach
  3.4. Optimization Algorithm
 4. Improved Methods
  4.1. Adjusting the log transform
  4.2. Avoiding bias field overestimation
 5. Results and Discussion
 6. Conclusions
 References

키워드

저자

• Ahmed Al-Taie [ Jacobs University, Bremen, Germany, Computer Science Department, College of Science for women, Baghdad University, Baghdad, Iraq ]
• Horst K. Hahn [ Jacobs University, Bremen, Germany, Fraunhofer MEVIS, Bremen, Germany ]
• Lars Linsen [ Jacobs University, Bremen, Germany ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605