An improved Kernel-based fuzzy C-means algorithm with spatial information for brain MR image segmentation

Rong Xu, Jun Ohya

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose an improved Kernel-based Fuzzy C-means Algorithm (iKFCM) with spatial information to reduce the effect of noise for brain MR image segmentation. We use k-nearest neighbour model and a neighbourhood controlling factor by estimating image contextual constraints to optimize the objective function of conventional KFCM method. Conventional KFCM algorithms classify each pixel in image only by its own gray value, but the proposed method classifies by the gray values of its neighbourhood system. For this reason, the proposed iKFCM has a strong robustness for image noise in image segmentation. In experiments, some synthetic grayscale images and simulated brain MR images are used to assess the performance of iKFCM in comparison with other fuzzy clustering methods. The experimental results show that the proposed iKFCM method achieves a better segmentation performance than other fuzzy clustering methods.

本文言語English
ホスト出版物のタイトルIVCNZ 2010 - 25th International Conference of Image and Vision Computing New Zealand
DOI
出版ステータスPublished - 2010 12 1
イベント25th International Conference of Image and Vision Computing New Zealand, IVCNZ 2010 - Queenstown, New Zealand
継続期間: 2010 11 82010 11 9

出版物シリーズ

名前International Conference Image and Vision Computing New Zealand
ISSN(印刷版)2151-2191
ISSN(電子版)2151-2205

Conference

Conference25th International Conference of Image and Vision Computing New Zealand, IVCNZ 2010
CountryNew Zealand
CityQueenstown
Period10/11/810/11/9

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

フィンガープリント 「An improved Kernel-based fuzzy C-means algorithm with spatial information for brain MR image segmentation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル