A novel approach in multimodality medical image fusion using optimal shearlet and deep learning. Issue 4 (1st May 2020)

• Record Type: Journal Article
• Authors: Subbiah Parvathy, Velmurugan; Pothiraj, Sivakumar; Sampson, Jenyfal
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 4(2020)
• Page Start: 847
• Page End: 859
• Publication Date: 2020-05-01
• Subjects: deep learning -- discrete shearlet transform (DST) -- frequency transform -- fusion rules and monarch butterfly optimization (MBO) -- medical image fusion; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22436
• Languages: English
• ISSNs: 0899-9457

Abstract

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.