Qualification: 
M.Tech
v_devi@cb.amrita.edu
Phone: 
9042245405

Devi Vijayan currently serves as Assistant Professor in the Department of Electronics and Communication Engineering at Amrita School of Engineering, Coimbatore, since 2006. A native of Kollam District, Kerala, she obtained her Bachelor’s degree in Electronics and Communication Engineering from Cochin University in the year 2003 and Master’s degree in Computer Vision and Image Processing from Amrita Vishwa Vidyapeetham in the year 2006. Her areas of interest include Signal Processing, Image Processing and Pattern Recognition. Currently she is pursuing her Ph.D in the area of Biomedical Image Processing. She is an Associate Member of IETE.

Research Expertise

UG Projects

  • Detection and Classification of Exudates in Diabetic Retinopathy.
  • Change detection in mammogram images

PG Projects

  • PSO based density classifier for mammograms

Teaching

  • Digital Image Processing
  • Soft Computing
  • Computational Intelligence
  • Pattern Recognition
  • Signals and Systems
  • Digital Signal Processing

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2015

Journal Article

R. M. Kirubaa, Dr. Lavanya R., Kotwal, N. P., and Vijayan, D., “Change Detection In Mammogram Images Using Fuzzy C- Means Clustering”, International Journal of Applied Engineering Research, vol. 10, no. 11, pp. 29825-29834 , 2015.[Abstract]


Experts have estimated that breast cancer is diagnosed in about one out of every eight women. At present mammography is the most efficient tool for the screening of breast cancer and studies show that misinterpretation is an important cause of missing breast cancer. In this paper we propose a computer aided detection system to identify changes in temporal mammographic images which would aid radiologists in the early and accurate detection of mammographic lesions. This system involves pre-processing, registration, generation of difference image and the analysis of difference image to obtain the changed and unchanged regions of the lesion. The novelty of this research work is to effectively find changes in mammogram images obtained from consecutive screening rounds using fuzzy c-means (FCM) clustering. The efficiency of FCM is compared with K-means clustering using overall error (OE) and kappa coefficient (KC). Experimental results show that the proposed method is a better alternative to the K-means clustering method. These techniques have been tested on mammogram images obtained from a private hospital. More »»
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