Open AccessClustering based Multi-modality Medical Image Fusion
Author(s) -
Rashmi Dhaundiyal,
Amrendra Tripathi,
Kapil Joshi,
Manoj Diwakar,
Prabhishek Singh
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1478/1/012024
Subject(s) - image fusion , artificial intelligence , cluster analysis , computer science , wavelet transform , enhanced data rates for gsm evolution , wavelet , image (mathematics) , pattern recognition (psychology) , computer vision , modality (human–computer interaction) , image quality , fuzzy logic , multi source , representation (politics) , mutual information , fusion , data mining , mathematics , statistics , politics , political science , law , linguistics , philosophy
The unwanted data obtained through the medical image fusion is the main problem in biomedical applications, guided-image surgical and radiology. The Stationary Wavelet Transform (SWT) denoted the various advantages over conventional representation of imaging approach. In this research article we introduced innovative multi-modality fusion technique for medical image fusion based upon the SWT. In our approach it disintegrates of source images into approximation layers (coarse layer) and detail layers through the Stationary Wavelet Transform scheme, then applying of the Fuzzy Local Information C-means Clustering (FLICM) and Local contrast fusion approach to overcome the blurring effect, sensitiveness and conserve the quality evaluation in the distinguish layers. The demonstration shows that it preserves more detailed information in the source images and it enhances the more quality features and edge preserved of the final fused image obtained through the reconstruction procedure by recursive initial steps. The different methodologies with other techniques to evaluate performance such as mutual information, edge based similarity and blind image quality. This shows that in both the objective and subjective analysis our methodology results attained more supercilious performance.