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A novel approach in multimodality medical image fusion using optimal shearlet and deep learning
Author(s) -
Subbiah Parvathy Velmurugan,
Pothiraj Sivakumar,
Sampson Jenyfal
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22436
Subject(s) - shearlet , computer science , artificial intelligence , image fusion , pattern recognition (psychology) , thresholding , entropy (arrow of time) , image (mathematics) , benchmark (surveying) , fusion , deep learning , image quality , machine learning , linguistics , philosophy , physics , geodesy , quantum mechanics , geography
Abstract Multi‐modality medical image fusion (MMIF) procedures have been generally utilized in different clinical applications. MMIF can furnish an image with anatomical as well as physiological data for specialists that could advance the diagnostic procedures. Various models were proposed earlier related to MMIF though there is a need still exists to enhance the efficiency of the previous techniques. In this research, the authors proposed a novel fusion model based on optimal thresholding with deep learning concepts. An enhanced monarch butterfly optimization (EMBO) is utilized to decide the optimal threshold of fusion rules in shearlet transform. Then, low and high‐frequency sub‐bands were fused on the basis of feature maps and were given by the extraction part of the deep learning method. Here, restricted Boltzmann machine (RBM) was utilized to conduct the MMIF procedure. A benchmark dataset was utilized for training and testing purposes. The investigations were conducted utilizing a set of generally‐utilized pre‐enrolled CT and MR images that are publicly accessible. From the usage of fused low and high level frequency groups, the fused image can be attained. The simulation performance results were attained and the proposed model was proved to offer effective performance in terms of SD, edge quality (EQ), mutual information (MI), fusion factor (FF), entropy, correlation factor (CF), and spatial frequency (SF) with respective values being 97.78, 0.96, 5.71, 6.53, 7.43, 0.97, and 25.78 over the compared methods.