An Efficient Algorithm for Multimodal Medical Image Fusion based On Feature Selection and PCA Using DTCWT.

Background:   In the two past decades, medical image fusion has become an essential part of modern medicine due to the availability of numerous imaging modalities (MRI, CT, SPECT. etc). This paper presents a new medical image fusion algorithm based on DTCWT and uses different fusion rules in order to obtain a new image which contains more information than any of the input images. Methods: In order to improve the visual quality of the fused image, we propose a new image fusion algorithm based on Dual Tree Complex Wavelet Transform (DTCWT). Using different fusion rules in a single algorithm leads to a perfect reconstruction of the output (fused image).This combination will create a new method which exploits the advantages of each method separately. DTCWT present good directionality since it considers the edge information in six directions and provides approximate shift invariant. The goal of Principal Component Analysis (PCA) is to extract the most significant features (wavelet coefficients in our case) in order to improve the spatial resolution. The proposed algorithm fuses the detailed wavelet coefficients of input images using features selection rule. Results: We have conducted several experiments over different sets of multimodal medical images such as CT/MRI, MRA/T1-MRI; however, only results of two sets have been presented (due to pages-limit). The proposed fusion algorithm is compared to recent fusion methods presented in the literature (eight methods) in terms of visual quality and quantitatively using well known fusion performance metrics (five metrics). Results showed that the proposed algorithm outperforms the existing ones in terms of visual and quantitative evaluations. Conclusion: This paper focuses on image fusion of medical images obtained from different modalities. We have proposed a novel algorithm based on DTCWT in order to merge multimodal medical images. Experiments have been performed over two different sets of multimodal medical images. The results show that the proposed method significantly outperforms other techniques reported in the literature

An Efficient Algorithm for Multimodal Medical Image Fusion based on Feature Selection and PCA Using DTCWT (FSPCA-DTCWT)

Background: During the two past decades, medical image fusion has become an essential part ofmodern medicine due to the availability of numerous imaging modalities (e.g., MRI, CT, SPECT,etc.). This paper presents a new medical image fusion algorithm based on PCA and DTCWT,which uses different fusion rules to obtain a new image containing more information than any ofthe input images.Methods: A new image fusion algorithm improves the visual quality of the fused image, based onfeature selection and Principal Component Analysis (PCA) in the Dual-Tree Complex WaveletTransform (DTCWT) domain. It is called Feature Selection with Principal Component Analysisand Dual-Tree Complex Wavelet Transform (FSPCA-DTCWT). Using different fusion rules in asingle algorithm result in correctly reconstructed image (fused image), this combination willproduce a new technique, which employs the advantages of each method separately. The DTCWTpresents good directionality since it considers the edge information in six directions and providesapproximate shift invariant. The main goal of PCA is to extract the most significant characteristics(represented by the wavelet coefficients) in order to improve the spatial resolution. The proposedalgorithm fuses the detailed wavelet coefficients of input images using features selection rule.Results: Several experiments have been conducted over different sets of multimodal medicalimages such as CT/MRI, MRA/T1-MRI. However, due to pages-limit on a paper, only results ofthree sets have been presented. The FSPCA-DTCWT algorithm is compared to recent fusionmethods presented in the literature (eight methods) in terms of visual quality and quantitativelyusing well-known fusion performance metrics (five metrics). Results showed that the proposedalgorithm outperforms the existing ones regarding visual and quantitative evaluations.Conclusion: This paper focuses on medical image fusion of different modalities. A novel imagefusion algorithm based on DTCWT to merge multimodal medical images has been proposed.Experiments have been performed using two different sets of multimodal medical images. Theresults show that the proposed fusion method significantly outperforms the recent fusiontechniques reported in the literature

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

Satellite Image Fusion in Various Domains

In order to find out the fusion algorithm which is best suited for the panchromatic and multispectral images, fusion algorithms, such as PCA and wavelet algorithms have been employed and analyzed. In this paper, performance evaluation criteria are also used for quantitative assessment of the fusion performance. The spectral quality of fused images is evaluated by the ERGAS and Q4. The analysis indicates that the DWT fusion scheme has the best definition as well as spectral fidelity, and has better performance with regard to the high textural information absorption. Therefore, as the study area is concerned, it is most suited for the panchromatic and multispectral image fusion. an image fusion algorithm based on wavelet transform is proposed for Multispectral and panchromatic satellite image by using fusion in spatial and transform domains. In the proposed scheme, the images to be processed are decomposed into sub-images with the same resolution at same levels and different resolution at different levels and then the information fusion is performed using high-frequency sub-images under the Multi-resolution image fusion scheme based on wavelets produces better fused image than that by the MS or WA schemes

Comparative Analysis and Fusion of MRI and PET Images based on Wavelets for Clinical Diagnosis

Nowadays, Medical imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT), and Computed Tomography (CT) play a crucial role in clinical diagnosis and treatment planning. The images obtained from each of these modalities contain complementary information of the organ imaged. Image fusion algorithms are employed to bring all of this disparate information together into a single image, allowing doctors to diagnose disorders quickly. This paper proposes a novel technique for the fusion of MRI and PET images based on YUV color space and wavelet transform. Quality assessment based on entropy showed that the method can achieve promising results for medical image fusion. The paper has done a comparative analysis of the fusion of MRI and PET images using different wavelet families at various decomposition levels for the detection of brain tumors as well as Alzheimer’s disease. The quality assessment and visual analysis showed that the Dmey wavelet at decomposition level 3 is optimum for the fusion of MRI and PET images. This paper also compared the results of several fusion rules such as average, maximum, and minimum, finding that the maximum fusion rule outperformed the other two