A comparative review: Medical image fusion using SWT and DWT

Diwakar, Manoj; Tripathi, Amrendra; Joshi, Kapil; Sharma, Ayush; Singh, Prabhishek; Memoria, Minakshi; Kumar, Neeraj

doi:10.1016/j.matpr.2020.09.278

Cited by 33 publications

(6 citation statements)

References 10 publications

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“…The input image of size N × N is divided into four sub-images, each of size N/2 × N/2. Each sub-image contains information from different frequency components [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

2023

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Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.

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“…The input image of size N × N is divided into four sub-images, each of size N/2 × N/2. Each sub-image contains information from different frequency components [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

2023

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“…Four methods were employed to create an epidemic alarm system, including Random Forest Regression, Decision Tree Regression, Support Vector Regression, and Multiple Linear Regression [ 38 ]. Dhaka et al [ 39 ] analyzed the differences between the stationary wavelet transform (SWT) and the discrete wavelet transform (DWT) for different applications and found SWT outperforms DWT. According to a study by Dhaundiyal [ 40 ], a novel SWT-based multimodality fusion approach was presented for medical image fusion.…”

Section: Related Workmentioning

confidence: 99%

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform

Diwakar

Singh

Singh³

et al. 2023

Diagnostics

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Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

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“…in SWT, the signal is passed with a low pass filter and high pass filter in the same manner as DWT but there is no downsampling process applied to gain wavelet coefficients approximated and detailed coefficients. By inserting zeros at each level of the transform [18]. DWT suffers from shift variance and directional selectivity.…”

Section: Stationary Wavelet Transformmentioning

confidence: 99%

Enhancement Infrared-Visible Image Fusion Using the Integration of Stationary Wavelet Transform and Fuzzy Histogram Equalization

Khazal

Habeeb

2022

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Image fusion is the process of merging two or more images to obtain complementary features from source images. Imaging techniques in real-world applications provide images with a different texture than the other, where visible images provide spatial information while infrared images provide spectral information. Hence the importance of image fusion, which aims to combine spatial and spectral information in one image. Wavelet transform is a method used in the process of image fusion as feature extraction, and images are decomposed into a series of low and high-frequency subbands. Wavelet transform provides images with good representation and is a multi-resolution analysis. However, the resulting image after the wavelet-based fusion process has low-quality information which is blurry. In addition, infrared images by their nature suffer from blur. In this paper, a novel image fusion method has been proposed to enhance visible-infrared image fusion using the integration of stationary wavelet transform and fuzzy histogram equalization. Firstly, input the images. Secondly, preprocessing the images. Thirdly, stationary wavelet transform has been used for decomposing the images in 2 levels. Fourthly, Averaging fusion rule is used for fusing the approximation coefficients. Finally, fuzzy histogram equalization is used in reconstructing the level 2 process to obtain the final enhanced image. The performance of the proposed method is evaluated by using seven metrics that proved the superiority of the proposed method compared to the standard methods.

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A comparative review: Medical image fusion using SWT and DWT

Cited by 33 publications

References 10 publications

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform

Enhancement Infrared-Visible Image Fusion Using the Integration of Stationary Wavelet Transform and Fuzzy Histogram Equalization

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