Ph.D, M.E
+91 9865816977

Dr. R. Lavanya joined Amrita as a faculty of the department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore in the year 2006. She completed her B.E. degree in Electronics and Communication Engineering from Kumaraguru College of Technology in 2001 and M.E. in Applied Electronics from Coimbatore Institute of Technology in 2005. She earned her Ph.D. degree from Anna University, Chennai, in April 2015. Her Ph.D. thesis was on Computer Aided Diagnosis of Breast Cancer. Before joining Amrita, she worked as a lecturer in the department of ECE at Coimbatore Institute of Technology during the period 2001-2004. Her research areas of interest include Signal Processing, Biomedical Image Processing, Pattern Recognition and Soft Computing. She is currently working on Computer Aided Diagnosis of Diabetic Retinopathy. She is a member of the Institution of Electronics and Telecommunication Engineers, a professional body.

Research Expertise

Dr. Lavanya is currently guiding two Ph.D scholars. She is also guiding UG and PG students on their projects. A few projects being carried out under her guidance are listed below:

  • Content-based Image Retrieval for Diagnosis of Breast Cancer
  • Automated Grading of Non-Proliferative Diabetic Retinopathy

Some proposed research opportunities under her guidance, for prospective researchers include

  • Multimodal Ensemble for Characterization of Masses in Computer Aided Diagnosis of Breast Cancer
  • Bio-inspired Techniques for Image Registration


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


Publication Type: Journal Article

Year of Publication Title


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 »»


G. Menon, Dr. Palanisamy T., and Dr. Lavanya R., “Hardware Architecture for Variational Mode Decomposition for Breast Cancer Feature Extraction on Ultrasound Images”, International Journal of Applied Engineering Research, vol. 10, no. 7, pp. 16343-16354, 2015.[Abstract]

Ultrasound (US) imaging proved to be less harmful than the traditional mammography is used for diagnosing breast cancer and this has helped reduce the number of unnecessary biopsies.The most important feature of malignant breast lesion is its infiltrative nature in US images.This infiltrative nature having composed of the frequency components that are adjacent to the lower frequency band contains the local variances that are characterized by Variational Mode Decomposition (VMD).On comparison with the existing decomposition models such as Empirical Mode Decomposition (EMD) and Wavelet Transform (WT) which are known for their limitations like sensitivity to noise and sampling which could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition.To overcome these limitations, a non-recursive VMD was selected.In this paper, we have presented an algorithmbased on VMD and a suitable architectureto obtain the infiltrative nature of the malignant breast lesion from the US image.

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Dr. Lavanya R., Nagarajan, N., and Dr. Nirmala Devi M., “Computer-aided Diagnosis of Breast Cancer by Hybrid Fusion of Ultrasound and Mammogram Features”, Advances in Intelligent Systems and Computing, vol. 325, pp. 403–409, 2015.[Abstract]

Ultrasound images are increasingly being used as an important adjunct to X-ray mammograms for diagnosis of breast cancer. In this paper, a computer-aided diagnosis system that utilizes a hybrid fusion strategy based on canonical correlation analysis (CCA) is proposed for discriminating benign and malignant masses. The system combines information from three different sources, i.e., ultrasound and two views of mammogram, namely, mediolateral oblique (MLO) and craniocaudal (CC) views. CCA is employed on ultrasound-MLO and ultrasound-CC feature pairs to explore the hidden correlations between ultrasound and mammographic view. The two pairs of canonical variates are fused at the feature level and given as input to support vector machine (SVM) classifiers. Finally, decisions of the two classifiers are fused. Results show that the proposed system outperforms unimodal systems and state-of-the-art fusion strategies.

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Dr. Lavanya R., Nagarajan, N., and M. Nirmala Devi, “Multiview mammographic CADx system based on correlation analysis”, International Journal of Graphics and Image Processing, vol. 4, 2014.


Dr. Lavanya R. and Nagarajan, N., “Comparison of fusion schemes for two-view analysis of breast cancer using mammograms”, International Journal of Advanced Computer Technology, vol. 3, 2014.


Dr. Lavanya R., Nagarajan, N., and M. Devi, N., “False positive reduction in computer aided detection of mammographic masses using canonical correlation analysis”, Journal of Theoretical and Applied Information Technology, vol. 59, pp. 139-145, 2014.


Dr. Lavanya R. and Nagarajan, N., “Information fusion in CAD systems for breast cancer diagnosisusing mammography and ultrasound imaging: A survey”, Journal of Artificial Intelligence, vol. 7, pp. 113-122, 2014.[Abstract]

Breast cancer is the highest incident cancer in women and a serious threat to a woman's life. Early detection and treatment of breast cancer can reduce the mortality rate. Currently, mammography is widely employed for routine screening of breast cancer. Ultrasound imaging is used as an important adjunct to mammography, especially in the post-screening (diagnostic) phase. Irrespective of the imaging modality, several factors including the level of radiologists' expertise affect the accuracy of breast cancer detection and diagnosis. Computer Aided Detection/Diagnosis (CAD) systems are objective in nature as opposed to the subjective analysis made by radiologists. Many studies show that the use of a CAD system as a second reader has the potential to improve the accuracy of breast cancer detection and diagnosis. Recently, integration of information from multiple sources is gaining wide popularity in data analysis. Information fusion in CAD systems would serve to mimic the radiologist's practice of combining information from multiple mammographic views and from multiple imaging modalities like ultrasound imaging and mammography to arrive at better diagnostic decisions. This study reviews the literature on such CAD systems based on mammograms and ultrasound images for breast cancer detection and diagnosis.

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B. .A.Sabarish and Dr. Lavanya R., “Modified Leach Protocol for Wireless Sensor Network”, International Journal of Computer Applications, vol. 62, pp. 1-5, 2013.