Ramudu Kama

Work place: Department of ECE, Kakatiya Institute of Technology and Science, Warangal, 506015, India

E-mail: ramudukama20@gmail.com


Research Interests: Computer systems and computational processes, Computer Vision, Image Processing


Ramudu Kama was born in Andhra Pradesh, India in 1986. He received the B.Tech. and M.Tech. degrees from Acharya Nagrjuna University, Guntur and Kakatiya University, Warangal  in 2006 and 2008, respectively. In 2007, he joined in the department of ECE as lecturer, later promoted to assistant Professor in the year 2009 to till date at Kakatiya Institute of Technology and Science, Warangal, Telangana state, India. He spent five years as research scholar at ANU, Guntur during 2014-19 academic years. He is currently working as assistant Professor in KITS, Warangal. He has published over 28 refereed journal and conference papers in the areas of biomedical Image Processing as well as remote sensing Image processing. His research interests include Image processing, Computer Vision and Signal Processing for communications. He is the member of various professional bodies related ECE field such as MIETE, MISTE.

Author Articles
Segmentation of Soft Tissues and Tumors from Biomedical Images using Optimized K-Means Clustering via Level Set formulation

By Ramudu Kama Kalyani Chinegaram Ranga Babu Tummala Raghotham Reddy Ganta

DOI: https://doi.org/10.5815/ijisa.2019.09.03, Pub. Date: 8 Sep. 2019

Biomedical Image-segmentation is one of the ways towards removing an area of attentiveness by making various segments of an image. The segmentation of biomedical images is considered as one of the challenging tasks in many clinical applications due to poor illuminations, intensity inhomogeneity and noise. In this paper, we propose a new segmentation method which is called Optimized K-Means Clustering via Level Set Formulation. The proposed method diversified into two stages for efficient segmentation of soft tissues and tumor’s from MRI brain Scans Images, which is called pre-processing and post-processing. In the first stage, a hybrid approach is considered as pre-processing is called Optimized K-Means Clustering which is the combined approach of Particle Swarm Optimization (PSO) as well as K-Means Clustering for improve the clustering efficiency. We choose the ‘optimal’ cluster centers by Particle Swarm Optimization (PSO) algorithm for improving the clustering efficiency. During the process of pre-processing, these segmentation results suffer from few drawbacks such as outliers, edge and boundary leakage problems. In this regard, post-processing is necessary to minimize the obstacles, so we are implementing pre-processing results by using level-set method for smoothed and accurate segmentation of regions from biomedical images such as MRI brain images over existing level set methods.

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