Segmentation of Brain MRI Using K-means Clustering Algorithm

Mrs. P. Vijayalakshmi; Selvamani.K; Ms.M.Geetha

doi:https://doi.org/10.14445/22315381/IJETT-V1I1P24

Research Article | Open Access | Download PDF

Volume 1 | Issue 1 | Year 2011 | Article Id. IJETT-V1I1P24 | DOI : https://doi.org/10.14445/22315381/IJETT-V1I1P24

Segmentation of Brain MRI Using K-means Clustering Algorithm

Mrs. P. Vijayalakshmi,Selvamani.K,Ms.M.Geetha

Citation :

Mrs. P. Vijayalakshmi,Selvamani.K,Ms.M.Geetha, "Segmentation of Brain MRI Using K-means Clustering Algorithm," International Journal of Engineering Trends and Technology (IJETT), vol. 1, no. 1, pp. 113-115, 2011. Crossref, https://doi.org/10.14445/22315381/IJETT-V1I1P24

Abstract

To segment the medical image using K Means clustering algorithm. To propose an algorithm that can be better for large datasets and to find initial centroid. To compare the performance. An algorithm is described for segmenting MR brain image into K different tissue types, which include gray, white matter and CSF, and maybe other abnormal tissues. MR images considered can be either scale - or multi - valued. Each scale - valued image i s modeled as a collection of regions with slowly varying intensity plus a white Gaussian noise. The proposed algorithm is an adaptive K - means clustering algorithm for 3 - dimensional and multi - valued images. Each iteration consists of two steps: estimate mean intensity at each location for each type, and estimate tissue types, Its performance is tested using patient data.

Keywords

algorithms, vessel, MRA, XRA, CT

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