Qualification: 
Ph.D, M.E
r_lavanya[at]cb[dot]amrita[dot]edu
Phone: 
+91 9865816977, +91 422 2685000 Ext. 5727

Dr. R. Lavanya joined Amrita as a Faculty of the department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, 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 guiding Ph. D. scholars and PG and UG projects. She has published many papers in International journals and conferences. She is a member of the Institution of Electronics and Telecommunication Engineers, a professional body.

Education

  • 2015: Ph. D. in Biomedical Image Processing
    Anna University
  • 2005: M. Tech. in Applied Electronics
    Anna University

Professional Experience

Year Affiliation
July 2015 till date Assistant Professor (Selection Grade), Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Guiding Projects
July 2008 to June 2015 Assistant Professor (Senior Grade), Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Guiding Projects
July 2007 to June 2008 Senior Lecturer, Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Guiding Projects
July 2006 to June 2007 Lecturer, Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Guiding Projects
June 6 to October 2004 Lecturer, Coimbatore Institute of Technology
Domain : Teaching and Research

Academic Responsibilities

SNo Position Class / Batch Responsibility
1. Program Coordinator PG - M.Tech Biomedical Engineering (since Jan 2015) Admission process, Mentoring students, Reviewing Dissertation, Curriculum Revision
2. Class Advisor 2010– 2014 Mentoring, Reviewing Dissertation
3. Class Advisor 2007-2010 Mentoring, Reviewing Dissertation
4. Academic Coordinator 2010-2012 Department level Coordination of Academic activities

Undergraduate Courses Handled

  1. Signals and Systems
  2. Digital Signal Processing
  3. Soft Computing
  4. Analog Communication
  5. Digital Communication

Post-Graduate / PhD Courses Handled

  1. Computational Medical Diagnostics
  2. Multidimensional Digital Signal Processing
  3. VLSI Signal Processing
  4. Soft Computing
  5. Signal Processing
  6. Biomedical Signal Analysis
  7. Biomedical Image Processing

Participation in Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Two-day workshop on Recent Trends in image Analysis Amrita School of Engineering, Coimbatore June 2009 Academia interaction and technical upgradation
2. Two-day workshop on Research Issues in Digital Image processing Dr.Mahalingam College of Engineering and Technology, Coimbatore November 2009 Academia interaction and technical upgradation
3. One-day workshop on MATLAB and Simulink for Engineering Applications Amrita School of Engineering, Coimbatore March 2013 Academia interaction and technical upgradation
4. Two-day Workshop on Signal and Image Processing Applications Using Xilinx System Generator Amrita School of Engineering, Coimbatore April 2014 Academia interaction and technical upgradation
5. International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems Noorul Islam College of Engineering, Kanyakumari November 2014 Paper presentation, Academia interaction and technical upgradation

Organizing Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Ten-day UGC sponsored Refresher course on VLSI Design Amrita School of Engineering, Coimbatore December 2007 Academia interaction
2. Four-day National Workshop on Signal Processing for Biomedical Applications Amrita School of Engineering, Coimbatore February 2011 Industry and academia interaction
3. Two-day National Workshop on Image Processing for Biomedical Applications Amrita School of Engineering, Coimbatore June 2015 Industry and academia interaction

Academic Research – PhD Guidance

SNo Name of the Scholar Specialization / Title Duration / Registration Status / Year
1. Ms. Devi Vijayan Biomedical Image Processing / Content-based Image Retrieval for Breast Cancer Diagnosis June 2015 Completed comprehensive exam / February 2017
2. Ms.G.Suguna Biomedical Image Processing / Computer Aided System for Diagnosis of Glaucoma January 2016 Completed comprehensive exam / October 2017

Academic Research – PG Projects

SNo Name of the Scholar Programme Specialization Duration Status
1. Dhadma Balachandran Biomedical Engineering Mass characterization in mammograms using an optimal ensemble classifier 2015-16 Completed
2. Dharani V Biomedical Engineering Improved Microaneurysm Detection in Fundus Images for Diagnosis of Diabetic Retinopathy 2016-17 Completed
3. Amala Nair Biomedical Engineering Enhanced Empirial Wavelet Transform for Automated Glauoma Diagnosis 2017-18 Completed
4. Sneha Susan George Biomedical Engineering Quadratic Spline Wavelet based Analysis of ECG for Arythmia Classification 2017-18 Completed
5. Praveena S Biomedical Engineering Multitask Learning for Diabetic Retinopathy Lesion Detetion 2018-19 Ongoing
6. Srinithi V Biomedical Engineering Combining Clinical and Non-clinical Approaches for Automated Glaucoma Diagnosis 2018-19 Ongoing

Sponsored Research

SNo Title Agency Amount Date of Submission Status
1. Deep Learning based Computer Aided Diagnosis System for Glaucoma Staging using Digital Fundus Photography CSIR Rs. 20,16393 May 1, 2018 Preliminary screening done

Publications

Publication Type: Conference Paper

Year of Publication Title

2018

V. Dharani and Dr. Lavanya R., “Improved Microaneurysm Detection in Fundus Images for Diagnosis of Diabetic Retinopathy”, in Advances in Signal Processing and Intelligent Recognition Systems, Cham, 2018.[Abstract]


This paper addresses the development of a computer-aided diagnosis (CAD) system for early detection of diabetic retinopathy (DR), a sight threatening disease, using digital fundus photography (DFP). More specifically, the proposed CAD system is intended for detection of microaneurysms (MA) which are the earliest indicators of DR; CAD systems for MA detection involve two stages: coarse segmentation for candidate MA detection and fine segmentation for false positive elimination. The system addresses the common challenges in candidate MA detection, which includes detection of subtle MAs and MAs close to each other and those close to blood vessels which leads to low sensitivity. The system employs four major steps. The first step involves preprocessing of the fundus images, which comprises of shade correction, denoising and intensity normalization. The second step aims at the segmentation of candidate MAs using bottom hat transform, thresholding and hit or miss transformation. The use of modified morphological contrast enhancement and multiple structuring elements (SEs) in the hit or miss transform has improved the detection rate of MAs. The proposed method has been validated using a set of 20 fundus images from the DIARETDB1 database. The Free Response Operating Characteristics (FROC) curve demonstrates that many MAs that are otherwise missed out are detected by the proposed CAD system.

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2018

S. N. Kumar, Dinesh, D., Siddharth, T., Ramkumar, S., Nikhill, S., and Dr. Lavanya R., “Selection of Features Using Particle Swarm Optimization for Microaneurysm Detection in Fundus Images”, in Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, 2018, vol. 2018-January, pp. 140-144.[Abstract]


Diabetic Retinopathy (DR) is a predominant factor leading to blindness for decades. The main reason for loss of vision is the damage to vasculature in the retina. The earliest signs of DR are microaneurysms which show up as tiny red spots on the retina. Early detection of this indicator helps the ophthalmologists to detect DR, which helps in preventing blindness. In this work, a series of image processing algorithms including pre-processing, and coarse segmentation using mathematical morphology are employed to detect initial candidates for microaneurysms. This is followed by fine segmentation for which a set of optimal features is estimated using Particle Swarm Optimization (PSO). Classification performance of naïve Bayes and Support Vector Machine (SVM) are compared. The set of 19 features selected using PSO-SVM has led to an accuracy of 99.92% compared to the PSO-naïve Bayes with 22 features and an accuracy of 93.31%. The proposed system could be employed for accurate and fast detection of microaneurysms and thereby would considerably lower the workload and time spent by ophthalmologists.

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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.

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2016

D. Balachandran and Dr. Lavanya R., “Mass Characterization in Mammograms using an Optimal Ensemble Classifier”, in 2016 IEEE Region 10 Conference (TENCON), 2016.[Abstract]


Mass is the most common indicator in mammograms, especially in the early stages of breast cancer. Due to subtle nature of the masses, there is a considerable overlap between the malignant and benign mass characteristics. In this work, a Computer Aided Diagnosis (CADx) system that employs ensemble classifier has been proposed to improve mass characterization. Genetic algorithm (GA), an optimization technique, was employed to select the optimal ensemble. Multicollinearity among classifiers has to be resolved while forming the ensemble. Combining the classifiers that are highly correlated will not guarantee an improved performance when compared to individual classifiers. Variation Inflation Factor (VIF) analysis is incorporated in this work for detecting multicollinearity among classifiers.

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Publication Type: Journal Article

Year of Publication Title

2018

A. C. Nair and Dr. Lavanya R., “Enhanced Empirical Wavelet Transform for Denoising of Fundus Images”, Communications in Computer and Information Science, vol. 837, pp. 116-124, 2018.[Abstract]


Glaucoma is an ophthalmic pathology caused by increased fluid pressure in the eye, which leads to vision impairment. The evaluation of the Optic Nerve Head (ONH) using fundus photographs is a common and cost effective means of diagnosing glaucoma. In addition to the existing clinical methods, automated method of diagnosis can be used to achieve better results. Recently, Empirical Wavelet Transform (EWT) has gained importance in image analysis. In this work, the effectiveness of EWT and its extension called Enhanced Empirical Wavelet Transform (EEWT) in denoising fundus images was analyzed. Around 30 images from High Resolution Fundus (HRF) image database were used for validation. It was observed that EEWT demonstrates good denoising performance when compared to EWT for different noise levels. The mean Peak Signal to Noise Ratio (PSNR) improvement achieved by EEWT was as high as 67% when compared to EWT.

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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 »»

2015

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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2015

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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2014

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.

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.

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.

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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2013

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.

Publication Type: Conference Proceedings

Year of Publication Title

2018

M. Ganesan, Dr. Lavanya R., and Sumesh, E. P., “A Survey on Ballistocardiogram to study the Mechanical Activity of Heart”, Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017. Institute of Electrical and Electronics Engineers Inc., pp. 0557-0561, 2018.[Abstract]


Ballistocardiogram (BCG) refers to mechanical activity of the heart. It is a non-invasive technique that provides information on cardiovascular forces. Few studies have demonstrated the correlation that BCG exhibits with the cardiac output and pathological condition of the heart. However there is no clear proof of the relationship between cardiac cycles and BCG wave characteristics. Different BCG acquisition techniques exist, with differing sensor positions that include arm, feet and spine. To use BCG for clinical applications, the differences in the parameter of signals using different methods have to be reduced and a global template as that of Electrocardiogram (ECG) has to be obtained. So there is a need to understand the way BCG is captured and the nature of the signal. In this paper we have focused on the survey of various techniques for acquiring BCG, denoising methods and classification techniques. Recent developments in BCG are discussed along with comparative studies. The purpose of this paper is to investigate the feasibility of BCG in the field of medical diagnosis.

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2017

Dr. Lavanya R., Swathi, O. N., and Ganesan, M., “Peak Detection and Feature Extraction for the Diagnosis of Heart Diseases”, Proceedings in IEEE International Conference in Computing, Communications and Informatics. 2017.[Abstract]


Patient monitors with arrhythmia detection will enhance the quality of living of human by aiding in prediction of diseases in much early stage. In this work we have developed an algorithm for classifying the ECG signals into normal and arrhythmic signal. Here we have detected the R peaks from denoised ECG signal with an accuracy of 97.56%. Extracted features from the signal in both time and frequency domain and the signals are classified into normal and abnormal signals using support vector algorithm. The accuracy of the algorithm is tested by applying on MIT-BIH arrhythmia database and we obtained an overall 80% classifier accuracy.

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2014

ShilpaGopalakrishnan and Dr. Lavanya R., “Bilateral analysis of Mammograms for False Positive Reduction using Eigen ROI Approach”, International conference on Trends in Technology for Convergence. 2014.

2014

D. Pramod, DasariVenkatesh,, VenkataKarthik, J., Raghunathan, R., Kiran, C. S. Sai, and Dr. Lavanya R., “Identification of Corresponding Lesions from Ipsilateral Mammograms using eigen ROI approach”, International Journal of Computational Intelligence and Informatics. 2014.

2014

Y. Anjana, .Vinutna, G., Kalimatha, N., Namrata, B., Abinaya, A., and Dr. Lavanya R., “Handling Missing Data In Ipsilateral Mammograms For Computer Aided Breast Cancer Diagnosis”, International Journal of Scientific & Engineering Research, vol. 5. 2014.[Abstract]


Breast cancer is the most fatal among the cancers detected in women. Mammography is the most common and efficient tool for early detection of breast cancer. In mammography images of breast are captured in two standard views namely Mediolateral oblique (MLO) view and Craniocaudal (CC) view. Radiologists generally analyze both the views during diagnosis. In recent years as the number of cases to be diagnosed is increasing significantly, computer aided diagnosis (CAD) systems were developed. In the datasets used by these systems, often there is a chance for the presence of missing values (MVs) in any one of mammographic views due to various reasons. Some of these include obscuration of mass by a dense breast tissue, region of interest being out of frame during the image acquisition etc. This results in the use of data from single view alone for diagnosis. But the diagnostic performance of CAD systems is better when multi-view data is used. In this paper the use of Iterative Singular Value Decomposition (ISVD) imputation is proposed to handle missing values in order to preserve the advantage of using multi-view data during the diagnosis. Classification accuracy and Kappa statistics are the metrics used to assess the performance of ISVD scheme for different percentages of MVs ranging from 1-15%. Experimental results demonstrated that diagnosis using multi-view following ISVD performed at least as well as and most of the time better than the systems using single view.

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2012

Dr. Lavanya R., Sunith, G. N., Pradeep, S., DivyaBonagiri,, Anusha, A., and AbhipsaSamantra, “Computer Aided Detection and Diagnosis of Masses in Dense Tissue Breasts”, Proceedings of International Conference on Information Technology, Electronics & Communications. 2012.[Abstract]


Mammogram is the widely accepted imaging method used for screening breast cancer. Computer aided detection (CAD) systems intend to provide assistance to the radiologists in detecting breast cancer. However the detection of masses by CAD system is prone to a high rate of false positives. Radiologists often use bilateral analysis ie, symmetry between left and right breast to improve detection performance. In this work, a computer aided bilateral image analysis based on eigen region of interest (ROI) approach is used in order to reduce the false positive rate. It has been found that the proposed method outperforms the conventional false positive reduction techniques.

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2010

Dr. Lavanya R. and .Saranya, B., “High Speed, Low Complexity, Folded, Polymorphic Wavelet Architecture Reconfigurable Hardware”, International Journal of Advanced Science and Technology, vol. 18. 2010.[Abstract]


The main aim of this paper is to design and implement a high speed, low complexity and polymorphic architecture for reconfigurable folded wavelet filters. 5/3 wavelet results are incorporated into the 9/7 data path which reduces the number of adders compared to other solutions and also allows on the fly switching between the filters. The proposed work is to improve the speed of this reconfigurable architecture. This is accomplished by scheduling. A weight based scheduling algorithm has been used in this paper. This is an analysis method to improve inter task communication as well as data dependencies among tasks which will reduce the overall communication overhead and processing time.

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2009

P. .L.Chidambaram, .B.Ganeshkumar, R., .Naren, M., .Raghuram, K., .Youvarajah, K., and Dr. Lavanya R., “Design of Multiple Description Image Coding System Applying Directional Lifting Based Transform”. 2009.