An Intensity- Texture Model Based K-Means For Mammogram Segmentation

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-24 Number-3
Year of Publication : 2015
Authors : K. Karteeka Pavan, Ch. Srinivasa Rao
  10.14445/22312803/IJCTT-V24P125

MLA

K. Karteeka Pavan, Ch. Srinivasa Rao "An Intensity- Texture Model Based K-Means For Mammogram Segmentation". International Journal of Computer Trends and Technology (IJCTT) V24(3):113-118, June 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Image segmentation is an important characteristic in many applications including medical imaging etc. K-means is the simple, efficient, clustering technique in medical image segmentation. One of the drawbacks in k-means algorithm does not use the spatial information of image space in the clustering process. This paper proposes a simple algorithm to combine intensity values and spatial information of image to determine regions of interest in mammograms. The spatial information of the image is incur using textural features by dividing the image into windows. Experimental results conducted on each image of MIAS database and on the mammograms collected from Jahnavi imaging, Guntur, A.P. The results demonstrated the accuracy and efficiency of the algorithm in identifying the masses of mammograms.

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Keywords
Segmentation, texture, intensity, k-means Mammogram, Region of interest.