Devi Vijayan

Assistant Professor, Electronics and Communication Engineering, School of Engineering, Coimbatore
Qualification: 
M.Tech
Email: 
v_devi@cb.amrita.edu
Phone: 
9042245405

Devi Vijayan obtained his B. Tech in Electronics and Telecommunication in 2003 from Cochin University and M. Tech in Computer Vision and Image Processing in 2006 from Amrita Vishwa Vidyapeetham, Coimbatore. She is currently pursuing her part time Ph. D in Biomedical Image Processing in the department of Electronics and Communication, Amrita Vishwa Vidyapeetham, Coimbatore. Her areas of interest include Signal Processing, Image Processing and Pattern Recognition. She is an Associate Member of IETE.

Education

  • Pursuing: Ph. D. in Biomedical Image Processing
    Amrita Vishwa Vidyapeetham
  • 2006: M.Tech. in Computer Vision & Image Processing
    Amrita Vishwa Vidyapeetham
  • 2003: B.Tech in Electronics and TeleCommunication
    Cochin University

Professional Experience

Year Affiliationn
August 1, 2012 - Till date Assistant Professor (Sr. Gr.), Amrita Vishwa Vidyapeetham
Domain : Teaching & Research
July 3, 2006- July 31, 2012 Assistant Professor , Amrita Vishwa Vidyapeetham
Domain : Teaching & Research
March 17, 2004 - May 3, 2004 Guest Lecturer, College of Engineering, Adoor
Domain : Teaching

Academic Responsibilities

Position Class / Batch Responsibility
Class Adviser 2016 - 20 To monitor the academic activities, Counseling has to be carried out both academically as well as personally.

Undergraduate Courses Handled

  1. Digital Design
  2. Electronic Circuits I
  3. Electronic Circuits II
  4. Electrical Technology
  5. Signals and Systems
  6. Digital Signal Processing
  7. Image Processing
  8. Pattern Recognition Techniques and Algorithms
  9. Introduction to Soft Computing
  10. Solid State Devices
  11. Introduction to Digital Signal Processing

Post-Graduate / PhD Courses Handled

  • Biomedical Image Processing (BME)
  • Medical Imaging Techniques (BME)

Participation in Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Workshop on Deep Learning – Academic and Research Perspectives PSG Tech. Coimbatore 24-25 January 2018 Insight into Emerging Techniques
2. Workshop on Soft Computing Techniques Amrita Vishwa Vidyapeetham 17-18 July 2015 Enhanced Teaching and Research
3. ISTE Workshop on Signals & Systems IIT Kharagpur 02-12 January 2014 Faculty development programme

Organizing Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Workshop on Biomedical Signal Acquistion and Conditioning Amrita Vishwa Vidyapeetham 17-19 December 2015 Understanding the intricacies in signal acquisition and conditioning
2. Workshop on Image Processing for Biomedical Applications Amrita Vishwa Vidyapeetham 12-13 June 2015 Enhanced Teaching and Research
3. Workshop on Image Processing for Biomedical Applications Amrita Vishwa Vidyapeetham 16-17 December 2016 Enhanced Teaching and Research

Academic Research – PG Projects

SNo Name of the Scholar Programme Specialization Duration Status
1. Sneha P. Simon BME Image Processing and Machine Learning 2015-2016 Completed
2. S. P. Sneha BME Image Processing and Machine Learning 2016-2017 Completed
3. K. Kiruthika BME Image Processing and Machine Learning 2018-2019 Ongoing

Instructional Materials Developed

SNo Name & Description Outcome
1. Signals and Systems Lab manual A clear framework for the smooth conduct of lab

Publications

Publication Type: Journal Article
Year of Publication Title

2020

D. Vijayan and Dr. Lavanya R., “Hybrid Local Descriptor for Improved Detection of Masses in Mammographic CAD Systems”, International Journal of Advanced Intelligence Paradigms, Inderscience (Accepted), 2020.

2015

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 »»

Publication Type: Conference Paper
Year of Publication Title

2017

S. Simon, Dr. Lavanya R., and Vijayan, D., “PSO Based Density Classifier for Mammograms”, in The 16th International Conference on Biomedical Engineering, Singapore, 2017.[Abstract]


Breast cancer is the major cancer diagnosed in both, developed and developing countries. Early detection and treatment of breast cancer is necessary to moderate the associated fatality rates. Mammography is the widely accepted modality for screening breast cancer. Breast density is considered one of the major risk indicators for Breast cancer. Nevertheless, low contrast and subtle nature of abnormalities reduces the sensitivity of mammograms, especially in dense breast. In this paper we present an automatic method for breast density classification based on two level cascaded support vector machine (SVM) classifiers. Particle Swarm Optimization (PSO) has been employed for SVM parameter optimization that resulted in a low set up time for building the system. The proposed system was tested on mini-MIAS database, and an overall classification accuracy of 82% was achieved. Also the system could prompt the radiologists on high-risk cases, thereby gaining more attention from them for diagnosis of such cases.

More »»
Faculty Research Interest: 
Digital Electronics, Signal Processing, Image Processing, Pattern Recognition
 
             
Branding Privacy Policy Terms of Use Ragging/Grievance
© Amrita Vishwa Vidyapeetham   Amritapuri | Bengaluru | Chennai | Coimbatore | Kochi | Mysuru
AVVP