Improved Bat Algorithm using Super-Region Volume Segmentation for Medical Images
Bhawna Nigam1, Ramakrishnan M Ramanathaiah2, Basavaprasad B.3
1Dr. Bhawna Nigam, Assistant Professor, Department of Information Technology, Institute of Engineering & Technology, Devi Ahilya University, Indore, India.
2Ramakrishnan M Ramanathaiah, Director-SAP Practice-Analytics Big-Data-Cloud, Miracle Software Systems, USA.
3Dr. Basavaprasad B., Assistant Professor, Govt. First Grade College, Raichur, India.
Manuscript received on 14 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 3564-3567 | Volume-8 Issue-10, August 2019 | Retrieval Number: J97690881019/19©BEIESP | DOI: 10.35940/ijitee.J9769.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medical imaging has been the highly active research area from the past few dec-ades. With the intention of consolidating the automatic approaches of power of the segmentation with the experienced and capable of annotating biological sam-ples manually, provides a new board which is termed as Super-Region Volume Segmentation (SuRVoS). SuRVoS purifies the estimation by utilizing Markov Random Field (MRF) formulation, which in turn considers neighboring labels in order to estimate precisely consistent. Nevertheless, in the MRF formulation model, various separations play a vital part in order to raise the exactness of out-put of the segmentation. Hence, identifying that partition number is an important task and also it is a difficult problem. In proposed work, the edge weight is seg-regated into two disjoints sets, which means cut and off into all feasible disjoint segments. The proposed energy function is reduced by utilizing Enhanced Bat (EB) algorithm over the MRF and the standard deviation calculation among two edges. In our work, the improved SuRVoS gives good segmentation output by generating good objective function values in the form of best supervoxels. So, it raises the accuracy of image segmentation greater instead of the earlier methods. The performance metrics were computed namely accuracy, precision, recall, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and correlation values by utilizing the proposed method. The experimental output gives the greater seg-mentation accuracy which is accomplished by the proposed method.
Keywords: Segmentation, BAT, SuRVoS, MRF, Super-Regions.
Scope of the Article: Algorithm Engineering