Multimodal Medical Image Fusion Using Discrete Ripplet Transform and Intersecting Cortical Model

Kavitha, C.T.; Chellamuthu, C.; Rajesh, R

doi:10.1016/j.proeng.2012.06.175

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…So far, there have been lots of modified models, such as ICM [33], unit linking PCNN model [34], fast-linking PCNN model [35], SCM [36]. It has been proved that the SCM has lower computational complexity and higher accuracy rates for image segmentation when compared with other models [15]; so SPCNN which is derived from SCM model and showed in Figure 2 could be used in this paper and is described as followed:…”

Section: B Simplified Pcnnmentioning

confidence: 99%

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

Jiao

Zhang

2019

IEEE Access

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This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results. INDEX TERMS Image segmentation, gravitational search algorithm, pulse coupled neural network, edge matching, noise control.

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Section: B Simplified Pcnnmentioning

confidence: 99%

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

Jiao

Zhang

2019

IEEE Access

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“…The majority of the MIF techniques based on PCNN use the normalized single value of the pixel in the spatial domain or the coefficient in the transform domain as the feeding input to the PCNN which leads to contrast reduction and loss of directional information respectively [19,[21][22][23][24]. Moreover, using a single pixel/coefficient value as stimuli for a PCNN neuron is not effective, since the human visual system is more sensitive to the variations in images such as edges, contours and directional features.…”

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

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“…In recent years, researchers proposed several image fusion algorithms based on transform domain and PCNN. In Literature [16], a fusion algorithm based on Discrete Ripplet Transform (DRT) and Intersecting Cortical Model (ICM) for multimodal medical image is proposed.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Framework Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

Wang¹,

Ma²,

Zhan³

et al. 2013

JMM

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In this paper, we present a new medical image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM). The flexible multi-resolution, anisotropy, and directional expansion characteristics of NSCT are associated with global coupling and pulse synchronization features of SCM. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Firstly, maximum selection rule (MSR) is used to fuse low frequency coefficients. Secondly, spatial frequency (SF) is applied to motivate SCM network rather than using coefficients value directly, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. The effectiveness of the proposed algorithm is achieved by the comparison with existing fusion methods

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Multimodal Medical Image Fusion Using Discrete Ripplet Transform and Intersecting Cortical Model

Cited by 10 publications

References 8 publications

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

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

Multimodal Medical Image Fusion Framework Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

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