Loading [MathJax]/extensions/MathZoom.js
Unsupervised abnormalities extraction and brain segmentation | IEEE Conference Publication | IEEE Xplore

Unsupervised abnormalities extraction and brain segmentation


Abstract:

In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of Computed Tomography (CT) brain im...Show More

Abstract:

In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of Computed Tomography (CT) brain images. The ultimate goal of segmentation is to obtain three segmented images, which are the abnormalities, cerebrospinal fluid (CSF) and brain matter respectively. The proposed approach contains of two phase-segmentation methods. In the first phase segmentation, the combination of k-means and fuzzy c-means (FCM) methods is implemented to partition the images into the binary images. From the binary images, a decision tree is then utilized to annotate the connected component into normal and abnormal regions. For the second phase segmentation, the obtained experimental results have shown that modified FCM with population-diameter independent(PDI) segmentation is more feasible and yield satisfactory results.
Date of Conference: 17-19 November 2008
Date Added to IEEE Xplore: 30 December 2008
ISBN Information:
Conference Location: Xiamen, China

References

References is not available for this document.