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
- Digital Design
- Electronic Circuits I
- Electronic Circuits II
- Electrical Technology
- Signals and Systems
- Digital Signal Processing
- Image Processing
- Pattern Recognition Techniques and Algorithms
- Introduction to Soft Computing
- Solid State Devices
- 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
| 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 »» |
| Year of Publication | Title |
|---|---|
|
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 »» |
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