Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space

Jin, Xin; Gao, Chen; Hou, Jingyu; Jiang, Qian; Zhou, Dongming; Yao, Shaowen

doi:10.1016/j.sigpro.2018.08.002

Cited by 71 publications

(30 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these figures, RGB effect was provided to the MRIs classification regions to represent different inherent patterns. The RGB effect is device-dependent in nature [54], [55], so the different color combinations were observed in the MRI classification regions described by the MINCR, MAXCR, AVGCR and J(MINCR, MAXCR).…”

Section: Discussion On Pattern Classification and Visualizationmentioning

confidence: 99%

A Hybrid Fuzzy Clustering Approach for the Recognition and Visualization of MRI Images of Parkinson’s Disease

2020

View full text Add to dashboard Cite

Parkinson's disease (PD) is one of the serious diseases in the neurodegenerative disease group, whose early stage pre-diagnosis is still very tedious. Radiologists and medical practitioners mostly depended on the analysis of PD patients' magnetic resonance images (MRIs) to identify this disease. Due to presence of grayscale features and uncertain inherited information in MRIs, their pattern recognition and visualization were very complex. With this motivation, a new method for analyzing and visualizing patterns in MRI images was presented in this study. For this purpose, this study adopted fuzzy information gain (FIG) function and Kmeans clustering algorithm. The FIG function was used to quantify the fuzzified pixels information, whereas K-means clustering algorithm was employed to cluster those fuzzified pixels information. Finally, changes in MRIs were recognized and classified into three distinct regions, viz., the minimum changed region (MINCR), the maximum changed region (MAXCR) and the average changed region (AVGCR). Experimental results were provided by comparing PD patients' segmented MRIs with seven well-known image segmentation methods, including adaptive threshold method, watershed method, gray threshold method, fuzzy based method, K-means clustering algorithm, adaptive K-means clustering algorithm and fuzzy c-means (FCM) algorithm. The proposed method achieved an average mean squared error of 63.49, peak signal-to-noise ratio of 30.14 and Jaccard similarity coefficient of 0.92 among nine MRIs of PD. The performance showed an improvement of 20.73%-32.94%, 3.54%-6.20% and 6.98%-64.29% over the average mean squared error, peak signal-to-noise ratio and Jaccard similarity coefficient, respectively compared to other image segmentation methods.

show abstract

Section: Discussion On Pattern Classification and Visualizationmentioning

confidence: 99%

A Hybrid Fuzzy Clustering Approach for the Recognition and Visualization of MRI Images of Parkinson’s Disease

2020

View full text Add to dashboard Cite

show abstract

“…If the number of resolution level was m, there were m+1 subbands could be obtained by NSP, whose sizes were all the same as source images. Then, the high-frequency subbands in each resolved level were resolved by SF at n orientations to obtain 2 n directional subbands [19] [20]. The resolution processing of nonsubsampled shearlet transform (NSST) is revealed in Fig.…”

Section: Nonsubsampled Shearlet Transformmentioning

confidence: 99%

A multisource image fusion method for multimodal pig-body feature detection

Zhong¹,

Wang²,

Gao³

2020

KSII TIIS

View full text Add to dashboard Cite

“…The status of image fusion technology is irreplaceable in the historical process of image technology. So far, image fusion has penetrated into various fields such as computer vision [1], medical image [2][3][4], and electricity [5]. Image fusion is mainly to combine the information of two or more related multi-source images into a single image through an appropriate algorithm.…”

Section: Introductionmentioning

confidence: 99%

Infrared and visible image fusion method of dual NSCT and PCNN

Chen

2020

PLoS ONE

View full text Add to dashboard Cite

To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image-IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

show abstract

Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space

Cited by 71 publications

References 39 publications

A Hybrid Fuzzy Clustering Approach for the Recognition and Visualization of MRI Images of Parkinson’s Disease

A Hybrid Fuzzy Clustering Approach for the Recognition and Visualization of MRI Images of Parkinson’s Disease

A multisource image fusion method for multimodal pig-body feature detection

Infrared and visible image fusion method of dual NSCT and PCNN

Contact Info

Product

Resources

About