A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Abstract

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

“…However it often does not provide perfect segmentation. Therefore, to properly estimate the threshold of homogeneity or heterogeneity of the brain regions, it is beneficial to combine all or most of the measures for better segmentation results [138][139][140][141]. For example, the interest of integrating prior knowledge and contextual information has opened up good tracks of research in the segmentation of medical images [47,142].…”

Automated Identification and Localization of the Inferior Vena Cava Using Ultrasound: An Animal Study

“…Such an approach is employed since it is sensitive to location and local shape, although the vessel borders could also be detected by segmentation methods. 27,28…”

“…27,28 2. The circle shifts vertically and away from the touching boundary determined in Step(1)in a small step, for example, two pixels.…”

A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

“…The process of region growing, introduced by Adams and Bischof, takes one seed (a starting pixel) and finds neighboring pixels using a threshold to categorize the pixels into regions [ 43 ]. Since then, several improvements have been made to the region-growing algorithms, mainly in the seed selection procedure [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. For example, Isa et al used k-mean clustering to modify seed selection of the edge detection algorithm analyzed on Pap smear images [ 44 ].…”