Improved fuzzy clustering segmentation for medical images

Title:

Improved fuzzy clustering segmentation for medical images

Creator:

Kannan , S. R., Ramathilagam , S., Pandiyarajan , R., Lian , Shiguo , and Sathya, A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:739a98f6-8dbd-4fac-9d2a-31197aa26c8c
uuid:739a98f6-8dbd-4fac-9d2a-31197aa26c8c

Subject:

Fuzzy c-means, image segmentation, medical image, and MRI

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The purpose of this paper is to develop some effective robust fuzzy c-means methods for segmentation of Brain Medical Images and Dynamic Contrast-Enhanced Breast Magnetic Images (DCE-BMRI). Segmentation is a difficult task and challenging problem in the brain and breast medical images for diagnosing Breast and Brain cancer related diseases before the image goes for treatment plan. This paper presents three new effective fuzzy clustering techniques: Robust KFCM (Kernel Fuzzy C-Means) with spatial information, Effective Robust FCM based Kernel function, Modified fuzzy c-means algorithm with weight Bias Estimation. In experiments, the presented methods are compared with other reported methods. Experimental results on both breast and brain MR images show that the proposed algorithms have better performance than the standard algorithms. Thus, the proposed method is capable of dealing with the intensity in-homogeneities and noised image effectively.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:3

Harvested from:

CDK

Metadata only:

false