Multiresolution morphological fusion of MR and CT images of the human brain

Matsopoulos, George K.; Marshall, Stephen; Brunt, J. N. H.

doi:10.1049/ip-vis:19941184

Cited by 95 publications

(48 citation statements)

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“…To verify the superiority of the proposed fusion method, we compare it with five other fusion algorithms, including Gradient Pyramid based algorithm (GRP) [3], Morphological Pyramid based algorithm (MP) [4], Hierarchical Pyramid based algorithm (FSD) [5],and Discrete Wavelet Transform based algorithm(DWT) [6] .The decomposition level in GRP, MP, FSD and DWT is 6. The proposed algorithm was implemented on MATLAB 2009b, and the decomposition level of MMCA is 3.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Multi-focus Image Fusion Algorithm Based on Multilevel Morphological Component Analysis

Wang¹,

Li²

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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Abstract. This study proposed a novel image fusion algorithm based on weighed multilevel morphological component analysis (FWMMCA). First, morphological component analysis is improved into a new multi-scale decomposition algorithm-multilevel morphological component analysis (MMCA). Then, feature vectors are extracted from MMCA sub images, which are used to reflect brightness, texture regularity, energy, and randomness of those images. Moreover, the feature vectors are innovatively used as weight in fusion rules, to propose a weighted fusion rules. Sub images are fused via those fusion rules, which are finally reconstituted into fused image.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Multi-focus Image Fusion Algorithm Based on Multilevel Morphological Component Analysis

Wang¹,

Li²

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques [10,11], morphological methods [3,4,5], discrete wavelet transform [12,13,14] and neural network fusion [15]. The different classification of image fusion involves pixel, feature and symbolic levels [16].…”

Section: Overviewmentioning

confidence: 99%

“…For that purpose we propose enhanced multiscale convolution and morphology method, which we have introduced in [2]. Methods for image fusion using multiscale morphology have been broadly discussed in [3,4,5]. As an effect of fusing algorithm we obtain a height map and the reconstructed focused image with a very deep depth-offield.…”

Section: Introductionmentioning

confidence: 99%

Modeling of 3D Scene Based on Series of Photographs Taken with Different Depth-of-Field

Denkowski

Chlebiej

Mikołajczak

2008

Computational Science – ICCS 2008

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Abstract. This paper presents a method for fusing multifocus images into enhanced depth-of-field composite image and creating a 3D model of a photographed scene. A set of images of the same scene is taken from a typical digital camera with macro lenses with different depth-of-field. The method employs convolution and morphological filters to designate sharp regions in this set of images and combine them together into an image where all regions are properly focused. The presented method consists of several phases including: image registration, height map creation, image reconstruction and final 3D scene reconstruction. In result a 3D model of the photographed object is created.

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques (Burt (1984); Toet (1989)), morphological methods (Ishita et al (2006); Mukopadhyay & Chanda (2001); Matsopoulos et al (1994)), discrete wavelet transform (Li et al (1995); Chibani & Houacine (2003); Lewiset al (2007)) and neural network fusion (Ajjimarangsee & Huntsberger (1988)). The different classification of image fusion involves pixel, feature and symbolic levels (Goshtasby (2007)).…”

Section: Introductionmentioning

confidence: 99%