Echocardiography image enhancement using texture-cartoon separation. (July 2021)

• Record Type: Journal Article
• Authors: Jalali, Mohammad; Behnam, Hamid; Shojaeifard, Maryam
• Is Part Of: Computers in biology and medicine. Volume 134(2021)
• Publication Date: 2021-07
• Subjects: Texture-cartoon separation -- Speckle reduction -- Convolutional dictionary -- Image enhancement -- Echocardiography; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104535
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: Convolutional sparse coding helps decomposing echocardiography images to texture and cartoon subcomponents. Grid search leads to design dictionary structure before sparse coding the image. Image quality enhances while masking echocardiography image with the respective cartoon image. Synthetic images resembling echocardiography images are used to evaluate despeckling. Speckle reduction increases ultrasound image segmentation accuracy.