Qualification: 
M.Tech, B-Tech
v_gayathri@cb.amrita.edu

Gayathri V. currently serves as Assistant Professor at the Department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore Campus. She received her B. Tech. degree in Computer Science and Engineering from Government University College of Engineering, Kariavattom, Trivandrum and M. Tech. degree in Computer Vision and Image Processing from Amrita Vishwa Vidyapeetham, Coimbatore. Her areas of interest include Image Processing, Medical Image Analysis, Pattern Recognition and Machine Learning.

Publications

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2018

Conference Proceedings

S. Krishnan, B.A. Sabarish, Gayathri V., and Dr. Padmavathi S., “Enhanced Defogging System on Foggy Digital Color Images”, Computational Vision and Bio Inspired Computing, Part of the Lecture Notes in Computational Vision and Biomechanics book series, vol. 28. Springer International Publishing, Cham, pp. 488-495, 2018.[Abstract]


Images which are captured using camera can cause degradation in images by the effect of climatic conditions such as haze and fog. Image restoration makes a notable change in performing different application of computer vision and pattern recognition. The main aim of this paper is to improve the effect of fog and hazy images compared to the existing methods. The enhanced defogging system [EDS] consists of different image improvement techniques with a Dark Channel Prior [DCP] Algorithm to estimate the amount of fog is there in the images and transmission as well. Fusion based fog removal will reduce the amount of haze remained in those images. Experiments were done more than 100 images and the results are discussed below.

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Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

Gayathri V. and Menon, H. P., “Vasculature Detection from Retinal Color Fundus Images using Linear Prediction Residual Algorithm”, International Journal of Pure and Applied Mathematics, vol. 114, no. 12, pp. 171-178, 2017.[Abstract]


Automatic vasculature detection from color fundus images of retina has a significant role in automated diagnosis. Retinal vasculature identification has received importance off late , as it is an important anatomical structure in the analysis of retinopathy. Retinal vasulature can be obtained using any of the basic edge detection techniques but the challenge faced here is in identification of minituare blood vessels . In this paper the applicability of linear prediction residual algorithm for vasculature detection has been analysed. In this case the pixels which fail the prediction are considered as the vasculature edges and are then extracted from the fundus image. The results obtained show that minute blood vessels have also been identified using the proposed approach.

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2015

Journal Article

P. S and Gayathri V., “Comparison of the Performance of Linear Prediction Residual Algorithm on Different Medical Images”, International Journal of Applied Engineering Research, vol. 10, 7 vol., no. 17, 2015.

2014

Journal Article

Gayathri V., Menon, H. P., and Narayanankutty, K. A., “Edge Extraction Algorithm using Linear Prediction Model on Dental X-ray Images”, International Journal of Computer Applications (0975 – 8887), vol. 100, 19 vol., no. 19, pp. 0975-8887, 2014.[Abstract]


This paper focuses edge extraction from dental x-ray images for the root canal procedure, using the linear prediction (LP). The major issues of processing the dental X-ray images are caused due to the misalignment and the variation in the contrast, by the very modality of acquisition. Also the differences in the shapes and orientations of the teeth pose yet another difficulty in the processing. Thus, in order to overcome these challenges, the LP residual based approach is used in this paper to obtain better root canal edge information. In the present work, the input image is processed by the 10th order LP method to obtain LP residual image. The LP residual of the input image is found to provide better edges as compared to the conventional methods. Also the edge map obtained by the LP method is compared with previous work [7] on zero frequency resonator (ZFR) based edge extraction and is found to give a better edge map. Effectiveness of the LP residual method is confirmed by the visual inspection of the edge map and also from the subjective evaluation

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2014

Journal Article

Gayathri V. and Menon, H. P., “Challenges in Edge Extraction of Dental X-Ray Images Using Image Processing Algorithms – A Review”, (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 5355-5358, 2014.[Abstract]


The paper focuses on the major challenges faced during the processing of misaligned dental x-ray image. The objective of the paper is to discuss about the challenges faced during the root canal edge extraction from dental x-ray images, which forms a pre-requisite step prior to many other advanced automatic processing of x-ray images. For the edge extraction process of dental x-ray images, the effect of image processing algorithms is discussed and analyzed in this paper. Since edge extraction is the foremost step of all the major processing, the performance of the edge extraction stage is carried over to the later stages of processing. Some of the major areas that require processing dental x-ray images are root canal treatment, infection and other malignancy diagnosis.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2014

Conference Paper

Gayathri V. and Dr. Hema Menon P., “Root Canal Edge Extraction Using Zero Frequency Resonance Filter in dental X-Ray Images”, in Proceedings of International Conference on Signal and Speech Processing (ICSSP-14), 2014, pp. 71-75.

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