Paper Info
Title
Echocardiography Image Enhancement Using Texture-Cartoon Separation
Abstract
Due to the speckled nature of cardiac ultrasound imaging, it is not easy to process and extract useful information directly from the acquired image. In this work, we have proposed a method to reduce the effect of speckle artifacts through the decomposition of echocardiography images into cartoon and texture components. The first component (i.e., cartoon image) contains image structures containing smooth areas and sharp edges, and the texture component is mainly composed of highly oscillating and repetitive patterns. To decompose the image into these two subcomponents, convolutional sparse coding has been utilized as a solid tool for solving the decomposition optimization function. The significant advantage of using convolutional sparse coding, compared to classical sparse coding methods, is image quality enhancement due to not using the block coding, making the classic solutions computationally feasible. The original image has been masked with the cartoon part leading to suppress speckle artifacts which result in image quality enhancement. Besides, it has been shown that using this speckle reduction scenario, considerable accuracy enhancement of the segmentation task can be achieved, compared to segmentation of the original image. Numerical results provide acceptable reasons to prove the efficiency of the proposed algorithm. Resulting echocardiography videos show a mean segmentation enhancement of 15.98 for Hausdorff distance (in pixels) and 0.0632 for the Dice similarity coefficient.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104535
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Texture-cartoon separation, Speckle reduction, Convolutional dictionary, Image enhancement, Echocardiography
Journal
134
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Mohammad Jalali100.34
Hamid Behnam200.34
Maryam Shojaeifard300.68