Medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter

Xu, Liyun; Si, Yujuan; Jiang, Sai; Sun, Ying; Ebrahimian, Homayoun

doi:10.1016/j.bspc.2020.101885

Cited by 39 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This methodology is superior to another optimization algorithm, such as genetic algorithm (GA). Lina Xu et al [95] proposed optimized DWT-homomorphic filter based medical image fusion. The enhanced coefficients are optimized by a shark smell optimization, which is used to select optimized parameters.…”

Section: Hybrid and Optimization Algorithms Based Medical Image Fusionmentioning

confidence: 99%

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

View full text Add to dashboard Cite

A Medical imaging plays a vital role in medical diagnosis and clinical treatment. The biggest challenge in the medical field is the correct identification of disease and better treatment. Multi-modal Medical Image Fusion (MMIF) is the process of merging multiple medical images from different modalities into a single fused image. The main objective of the medical image fusion is to obtain a large amount of appropriate information (i.e., features) to improve the quality and makes it more informative for increasing clinical therapy for better diagnosis and clear assessment of medical related problems. The MMIF is generally considered with MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), MRA (Magnetic Resonance Angiography), T1-weighted MR, T2-weighted MR, X-ray, and ultrasound imaging (Vibro-Acoustography).This review article presents a comprehensive survey of existing medical image fusion methods and have been characterized as six parts: (1) Multi-modality medical images, (2) literature review process, (3) image fusion rules, (4) quality evaluation metrics for assessment of fused image, (5) experimental results on registered datasets and (6) Conclusion. In addition, this review article provides scientific challenges faced in MMIF and future directions for better diagnosis. It is expected that this review will be useful to establish a concrete foundation for developing more valuable fusion methods for medical diagnosis.

show abstract

Section: Hybrid and Optimization Algorithms Based Medical Image Fusionmentioning

confidence: 99%

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

View full text Add to dashboard Cite

show abstract

“…The SOA is considered a powerful optimization approach. It is extensively exploited in diverse cases, like solving mathematical functions [13]. In the SOA, rotational movement of sharks is an important operator for engrossing local optimums.…”

Section: 3proposed Soamodelmentioning

confidence: 99%

Shark Optimization Algorithm: Drug Dosage Estimation in Cancer Chemotherapy

Anbhazhagan¹

2022

JCMPS

View full text Add to dashboard Cite

Chemotherapy is a medicine that is frequently exploited to treat cancer; the cancer cells widen as well as propagate more rapidly than the other cells in body. Although chemotherapy is a capable technique to analyze several types of cancers, treatment involves threat as it reasons side effects because of inappropriate drug practice. Thus, this research proposes a robust controller to control the dosage of the drug which is performed in parameter estimation. Additionally, a Modified Regularized Error Function (MREF)-Extended Kalman Filter (EKF) is adopted to approximate tumor cells as well as it can be used for different circumstances. In addition, the overfitting problem happens at the time of the drug dosage estimation which is resolved by exploiting this method. Furthermore, to improve the proposed model performance, the primary condition of EKF is fine-tuned using Shark Optimization Algorithm (SOA). Finally, the preeminence of the developed technique is revealed regarding the error analysis.

show abstract

“…Sarmad et al proposed a method of fusing multimodal medical images by applying sparse representing and two-scale decomposing techniques on images [16]. Xu et al [17] proposed a method of fusing medical images using hybrid of wavelet-homomorphic filter and an algorithm of modified shark smell optimization. Polinati et al [18] introduced a method of fusing the information of the various image modalities such as speculation (SPEC), positron emission tomography (PET) and MRI using fusion rule of local energy maxima and empirical wavelet transform representation.…”

Section: Introductionmentioning

confidence: 99%

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

Trung¹,

Ngân²,

Tuấn³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Fusing medical images is a topic of interest in processing medical images. This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy. This fusion aims to improve the image quality and preserve the specific features. The methods of medical image fusion generally use knowledge in many different fields such as clinical medicine, computer vision, digital imaging, machine learning, pattern recognition to fuse different medical images. There are two main approaches in fusing image, including spatial domain approach and transform domain approachs. This paper proposes a new algorithm to fusion multimodal images. This algorithm is based on Entropy optimization and the Sobel operator. Wavelet transform is used to split the input images into components over the low and high frequency domains. Then, two fusion rules are used for obtaining the fusing images. The first rule, based on the Sobel operator, is used for high frequency components. The second rule, based on Entropy optimization by using Particle Swarm Optimization (PSO) algorithm, is used for low frequency components. Proposed algorithm is implemented on the images related to central nervous system diseases. The experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level, the contrast, the entropy, the gradient and visual information fidelity for fusion (VIFF), Feature Mutual Information (FMI) indices.

show abstract

Medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter

Cited by 39 publications

References 37 publications

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Shark Optimization Algorithm: Drug Dosage Estimation in Cancer Chemotherapy

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

Contact Info

Product

Resources

About