M.Tech, BE

Ms. Anisha Radhakrishnan joined Amrita School of Engineering, Coimbatore in July 2014. She received her BE degree in Computer Science and Engineering from Annamalai University, and M Tech. degree in Computer Science and Engineering from Karunya University. She currently serves as Assistant Professor in the department of Computer Science and Engineering, Amrita School of Engineering. Her areas of interest include Evolutionary computing, Image processing


Publication Type: Conference Paper

Year of Publication Title


N. Chithirala, Natasha, B., Rubini, N., and Anisha Radhakrishna, “Weighted Mean Filter for removal of high density Salt and Pepper noise”, in ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, 2016.[Abstract]

The essential constraint on the input images to any computer vision technology is its quality. Acquiring noise free digital images is a challenge as it depends on several factors. Developing algorithms to remove noise is one way to improve the image quality. Salt and pepper noise degrades the image. The challenge here is to restore the lost information without distorting the edges. This paper introduces a new algorithm that reduces high density salt and pepper noise from images. Restoration is done by calculating the weighted mean of the nearby pixels. Weights are assigned unsymmetrically to pre-processed and unprocessed pixels. The quality was judged based on the PSNR value. The algorithm restores information for highly corrupted images. Salt and pepper noise are usually filtered with variants of the median filter. This paper provides an alternate way for noise reduction. © 2016 IEEE.

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Publication Type: Journal Article

Year of Publication Title


Anisha Radhakrishna and Divya, M., “Survey of data fusion and tumor segmentation techniques using brain images”, International Journal of Applied Engineering Research, vol. 10, pp. 20559-20570, 2015.[Abstract]

Medical image fusion combines several medical images from different modalities to a single image. It gives more quality image and clinical information dropping redundancy. The fusion methods have proved better information accuracy and thereby increase clinical applicability like diagnosis, segmentation, surgery planning etc. MRI has gained wide acceptance due to its ability to provide tissue details. This survey article provides list of methods for the image fusion and MRI brain segmentation. The image fusion review is mainly based on three levels 1) pixel level, feature level and decision level. 2) Widely used techniques for image fusion. 3) Techniques for MRI brain segmentation. This paper concludes that these methods will help physician in medical diagnosis and analysis within short span of time. © Research India Publications.

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