Ranga Babu Tummala

Work place: Department of ECE, RVR&JC College of Engineering, Guntur, 522019, India

E-mail: trbaburvr@gmail.com


Research Interests: Computer systems and computational processes, Pattern Recognition, Embedded System, Image Compression, Image Manipulation, Image Processing


Tummala Ranga Babu obtained his Ph.D. in Electronics and Communication Engineering from JNTUH, Hyderabad, M.Tech in Electronics & Communication Engineering (Digital Electronics & Communication Systems) from JNTU College of Engineering (Autonomous), Anantapur, M.S.(Electronics & Control Engineering) from BITS, Pilani and B.E. (Electronics and Communication Engineering) from AMA College of Engineering (Affiliated to University of Madras). He served at different positions at different colleges. He is currently working as Professor & Head of Department of Electronics & Communication Engineering. He is member of Executive Council of RVR & JC College of Engineering (Autonomous). He is acting as Chairman, Board of studies for ECE board for RVR & JC College of Engineering(Autonomous). He is an active member of SWECHA and FSMI. He is a member in various professional bodies like IEEE, IETE, ISTE, CSI, IACSIT. His research interests includes Image Processing, Embedded Systems, Pattern Recognition, Digital Communication.

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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