MRI and PET Image Fusion Using Fuzzy Logic and Image Local Features

Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Taha; Cheema, Tanweer Ahmad

doi:10.1155/2014/708075

Cited by 31 publications

(21 citation statements)

References 32 publications

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“…10, the proposed method delivers color information of PET image effectively without distorting MR image information. The fusion performance of MR/PET image pairs is required to be compared to the recent method [60] in the future research work. As shown in Table 2, the proposed method shows superior fusion performance with much greater scores of all quality metrics.…”

Section: Multimodal Image Fusion Resultsmentioning

confidence: 99%

Joint patch clustering-based dictionary learning for multimodal image fusion

2016

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Section: Multimodal Image Fusion Resultsmentioning

confidence: 99%

Joint patch clustering-based dictionary learning for multimodal image fusion

2016

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“…It also improves the quality of the output image, especially around the edges [183], by producing a more noticeable and more natural merged image. Some works have attempted to propose new approaches which go beyond the conventional context of fusion such as neural networks with pulse-coupled neural network (PCNN) [191,281], fuzzy logic [282], genetic algorithms (GA) [283] and independent component analysis (ICA) [284].…”

Section: Critical Discussion About Multimodal Fusion Techniquesmentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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“…Fuzzy logic is also applicable to image fusion. In this process, local features of the image are extracted and combined with fuzzy logic to compute weights for each pixel [115,116]. The fuzzy logic-based fusion rule is often used to cope with blurry image fusion.…”

Section: Fuzzy Logic Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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MRI and PET Image Fusion Using Fuzzy Logic and Image Local Features

Cited by 31 publications

References 32 publications

Joint patch clustering-based dictionary learning for multimodal image fusion

Joint patch clustering-based dictionary learning for multimodal image fusion

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

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