Multispectral image fusion based on the Multiwavelet and IHS transforms

Nizam, Sema; Telatar, Ziya

doi:10.1109/siu.2012.6204579

Cited by 1 publication

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fusion process of medical images is one solution for achieving high spectral and spatial contents in a singular medical image [3]. In this regard, various approaches have been introduced in the works, for example, the high-pass filtering technique [4], Principal Component Analysis (PCA) [5] and Intensity Hue and Saturation (IHS) [6]. MRI and CT medical images are widely used in medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…The entered input image is broken down to various frequency levels, and mixing schemes are then applied, changing the element coefficients with the detail components of the added input image. Although wavelet fusion gives high spectral quality, the spatial declaration is reduced because of subsampling when the remotely sensed images are used for land cover and use [1,5,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Liver Lesions and Acute Intracerebral Hemorrhage Detection Using Multimodal Fusion

Faragallah¹,

Muhammed²,

Taha³

et al. 2021

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Medical image fusion is designed to help physicians in their decisions by providing them with a preclinical image with enough information. Accurate assessment and effective treatment of the disease reduce the time it takes to relieve the symptoms of the disease. This article utilizes an effective data fusion approach to work on two different imaging modalities; computed tomography (CT) and magnetic resonance imaging (MRI). The data fusion approach is based on the combination of singular value decomposition (SVD) and the Fast Discrete Curvelet Transform (FDCT) techniques to reduce processing time during the fusion process. The SVD-FDCT data fusion approach is being tested with two multimodal medical image fusion applications. The first application concerns the detection of liver lesions and the second application concerns the early detection of acute intracerebral hemorrhage. Experimental tests demonstrate that not only the SVD-FDCT data fusion algorithm can treat the curved objects and edges effectively as the FDCT do. But also, the throughput of the fusion algorithm is comparable to related fusion algorithms such as principal component analysis (PCA), Transform and Discrete Wavelet Transform (DWT), dual-tree complex wavelet transform (DT-CWT), and Curvelet fusion algorithms.

show abstract