Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Li, Liangliang; Ma, Hongbing

doi:10.3390/e23050591

Cited by 25 publications

(13 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These transforms have been widely used in image processing, such as image denoising and image fusion. Nonsubsampled contourlet transform (NSCT) is the optimization model of contourlet [ 38 ], and it is a translation invariant, multiscale, and multidirectional transformation. NSCT is constructed by a nonsubsampled pyramid (NSP) and nonsubsampled directional filter bank (NSDFB).…”

Section: Methodsmentioning

confidence: 99%

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Jia

2022

Entropy

Self Cite

View full text Add to dashboard Cite

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

show abstract

Section: Methodsmentioning

confidence: 99%

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Jia

2022

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors employ MRI to examine the cerebral cortex property and exploit rs-fMRI to analyze the function network's graph metric. In [14], the authors establish that it is settled well with the multi-task learning method. In [15] devised a DL technique for every level of feature extracting process and fuzzy hyper-plane related least square twin SVM (FLS-TWSVM) for classifying the derived features for initial analysis of AD through removing sagittal plane slices from 3-D MRI imageries.…”

Section: Literature Reviewmentioning

confidence: 96%

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

View full text Add to dashboard Cite

Brain disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities including Positron Emission Tomography (PET), Structural Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, Graph CNN, Modified ResNet18, Non-linear SVM, ResNet50-SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.

show abstract

“…Liu et al [20] introduced a change detection method based on mathematical morphology and a k-means clustering model, and the accuracy of the change detection improved. The nonsubsampled contourlet transform (NSCT) and nonsubsampled shearlet transform (NSST) are widely used in image fusion and denoising [21][22][23][24][25][26][27]. Chen et al [28] introduced the NSCT-hidden Markov tree (NSCT-HMT) model to the remote sensing image change detection; Li et al [29] proposed a multitemporal remote sensing image change detection algorithm based on the NSCT denoising model.…”

Section: Sar Image Preprocessingmentioning

confidence: 99%

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Jia

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

show abstract

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Cited by 25 publications

References 49 publications

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Contact Info

Product

Resources

About