Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT

Abstract

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

3D multimodal cardiac data reconstruction using angiography and computerized tomographic angiography registration

“…Therefore, DWT with wavelet type Haar and level 3 was used for removing noise from angiograms in this work. The details of using DWT for noise removal are well defined in [ 3 , 23 ]. This step is shown in Eq ( 1 ).…”

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

“…The fused images contain their high spatial quality and preserve their spectral information content. Though, the disadvantage is the spectral degradation which reduces the contrast levels in the fused image and hence could affect negatively the classification results [43,44]. The transform domain fusion comprises multi-resolution [45], wavelet transform [46,47], Laplacian and Gaussian pyramid [48], Bandelet Transform, Contourlet [49,50], and Curvelet [51,52].…”

“…Additionally, these methods preserve more spatial features and have a better signal to noise ratio compared to pixel-based approaches. Though, the transform domain methods are more complex than approaches of the spatial domain and leads to inadequately fusion images when applied on images from different sensors [43,44,53,54].…”

Segmentation Methods of Echocardiography Images for Left Ventricle Boundary Detection

“…Previous works in our lab towards the goal of Cardiac Intervention Environment, Cardiovascular Information System, and Heart Diseases Diagnostic Systems has included an automatic coronary arterial tree extraction in angiograms , cardiac ultrasound fusion system development (Mazaheri et al, 2014), Wavelet enhancement for x-ray angiography , 3D Multimodal Cardiac Data Reconstruction in CT Angiography , CT Angiography components categorization and coronary artery enhancement , and some surveys including review on segmentation approaches (Mazaheri et al, 2013 and, and review on registration of cardiac images Mazaheri et al (2015).…”