An improved coupled dictionary and multi-norm constraint fusion method for CT/MR medical images

Wang, Lifang; Dong, Xia; Cheng, Xi; Lin, Suzhen

doi:10.1007/s11042-018-5907-7

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…The high frequency coefficient of MRI and PET images is combined with the transformation later using the averaging technique. Inverse DWT is performed to get the resultant fused output [28]- [31].…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

Bhavana

Kariputtaiah²

2023

IJECE

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In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.

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Section: Introductionmentioning

confidence: 99%