Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain. (July 2021)

• Record Type: Journal Article
• Authors: Diwakar, Manoj; Singh, Prabhishek; Shankar, Achyut
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Multi-modality -- Co-occurrence filter -- Sum modified Laplacian -- Local extrema; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102788
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A review on Multi-modal medical image is given. A new method is proposed in Shearlet domain. A new weighted fusion function is introduced. The results and comparative analysis are discussed. Abstract: The fusion of vital imaging information has turned into a primary issue for biomedical applications. In this paper, a new multi-modality medical image fusion method is proposed in the shearlet domain. In the proposed algorithm, input images are decomposed using Non-subsampled shearlet transform (NSST) to get low and high frequencies components. A novel procedure to decompose and combine the base layers and detail layers using the local extrema (LE) approach is used and fused using a Co-occurrence filter (CoF) in low-frequency components. In high-frequency components, an edge-preserving image fusion strategy is performed using sum modified Laplacian (SML) to combine the high-frequency coefficients. The experimental outcomes and comparative analysis are performed over the Multi-modal medical image dataset using proposed and existing methods. It is exhibited through test outcomes and assessments that the proposed technique beats the cutting-edge fusion strategies concerning edge preservation in subjective and objective assessment criteria.