Heterogeneous Medical Image Retrieval using Multi-Trend Structure Descriptor and Fuzzy SVM Classifier
M. Natarajan1, S. Sathiamoorthy2
1M. Natarajan, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
2S. Sathiamoorthy*, Tamil Virtual Academy, Chennai, India.
Manuscript received on 02 August 2019. | Revised Manuscript received on 06 August 2019. | Manuscript published on 30 September 2019. | PP: 3958-3963 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5332098319/2019©BEIESP | DOI: 10.35940/ijrte.C5332.098319
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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: This research work contributes a system for heterogeneeous medical image retrieval usiing Multi-trend structure descriptor (MTSD) and fuzzy support vector machine (FSVM) classifier. The MTSD encodes the local level structure in the form of trends for color, shape and texture information of medical images. Experimental results demonstrate thatt the fusion of MTSD and FSVM significantly increases the retrieval precisiion for heterogeneeous medical image dataset. The simplest Manhattan diistance is incorporated for measuring the similarity. The feasibility of thee proposeed system is extensively experimented on benchmark daataset and the experimental study clearly demonstrated that proposed fusion of MTSD with Fuzzy SVM gives significantly superior average retrieval precision.
Keywords: Multi-Trend Structure Descriptor, Manhattan Similarity Measure, Local Structure, Fuzzy Support Vector Machine.
Scope of the Article: Heterogeneous Wireless Networks