Open AccessHeterogeneous Medical Image Retrieval using Multi-Trend Structure Descriptor and Fuzzy SVM Classifier
Author(s) -
M. Natarajan*,
S. Sathiamoorthy
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5332.098319
Subject(s) - artificial intelligence , pattern recognition (psychology) , support vector machine , fuzzy logic , computer science , image retrieval , classifier (uml) , benchmark (surveying) , computer vision , image (mathematics) , geography , cartography
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 precision for heterogeneeous medical image dataset. The simplest Manhattan diistance is incorporated for measuring the similarity. The feasibility of thee proposed 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.