The art of medical image fusion: A survey

Galande, Ashwini; Patil, Ratna

doi:10.1109/icacci.2013.6637205

Cited by 24 publications

(23 citation statements)

References 8 publications

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“…In our proposed scheme, the decomposition parameters are set to levels = [1,2,4]. The pyramidal filter is set to 'pyrexc' and the directional filter is set to 'vk' in the NCST configuration.…”

Section: A Non-subsampled Contourlet Transformmentioning

confidence: 99%

“…Medical image fusion can be decomposed into two major steps: measurement of activity level and applying a suitable fusion rule. Activity level refers to the local energy or the amount of information present in an image pixel or coefficient [4]. It can be measured for a single pixel value or by taking into consideration the surrounding neighbors of the pixel.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

H.M.Ahmed¹,

Hassan²,

Badr³

2016

ijacsa

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Section: A Non-subsampled Contourlet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

H.M.Ahmed¹,

Hassan²,

Badr³

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…The content of the image can be improved by using various imaging tools that helps in mixing useful information by removing the excessive information from registered source images. Galande and Patil (2013) [15] addressed the combination of CT and MRI images, which is used for analysis purpose and the fused images obtained from fuzzy inference system helps in achieving the better result. Pure et al (2013) [16] proposed fast discrete curvelet transform that analyses the curved images, it is possible to fuse an MRI and CT image which helps in diagnosis of disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Fusion of MRI and CT Images of Brain Tumor – Comprehensive Survey

2018

IJRTER

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Abstract-Image fusion of MRI and CT images frames an integration of relevant information from a set of images into a single image, where the resultant fused image will be more informative and quantitative than any of the input images. In brain medical imaging, MRI image shows structural information of the brain without any functional data, whereas CT image describes functional information of the brain but with low spatial resolution and so on. These multi-modality images contain important information for the accurate and effective diagnosis of brain diseases. Thus, fusing various modalities of images in medical field into a distinct image with more detailed anatomical information and high spectral information is highly desired in clinical diagnosis. This work presents a detailed literature review done on image fusion and also the concepts and materials that helps for clear understanding of various fusion techniques.

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“…Image fusion is an interdisciplinary area of research and has received a lot of interest in academic, industrial, hospitals, manufacturing, robotics, military and computer vision [4], [5]. The spatial domain techniques such as PCA, averaging method have low spectral resolution whereas spectral domain such as DWT, Curve-let, SWT techniques have low spatial resolution therefore it degrades the quality of output image [6].…”

Section: Introductionmentioning

confidence: 99%

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Li¹,

Bhutto²,

Du³

et al. 2018

ijacsa

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Abstract-Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg's uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.

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The art of medical image fusion: A survey

Cited by 24 publications

References 8 publications

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

Fusion of MRI and CT Images of Brain Tumor – Comprehensive Survey

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

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