BEMD with Clustering Algorithm for Segmentation of Microarray Image

Lakshmana Phaneendra Maguluri; Sandeepraja Batchu; Eswar Patnala

Image segmentation is one of core challenging areas in image analysis. However, many segmentation algorithms have been developed for several applications. In some cases they encountered unsatisfactory results. For segmentation of microarray images clustering algorithms have been applied. Considering micro array image as analysis, micro array image contain noise and noise could affect the image segmentation results. In order to overcome this drawback, this we propose to combine the clustering algorithms’ with BI-Dimensional Empirical mode decomposition for segmentation of micro array images in order to reduce effect of noise. We call this method as Weighted Fuzzy C-means with Bi-Dimensional Empirical Mode Decomposition (WFCBEMD) for image segmentation. We use an adaptive local weighted averaging filter in the BEMD method for removing the noise in the image. Then the filtered image is finally with conventional K-means algorithm.

Select Volume / Issues:

• Volume 4 Issue 2, March. 2013

Year:

2013

Type of Publication:

Article

Keywords:

Bi Dimensional Empirical Mode Decomposition; Clustering Algorithms; DNA Micro Array Processing

Journal:

IJECCE

Volume:

4

Number:

2

Pages:

389-393

Month:

March