Automatical IVUS Image Segmentation and Detection Using BLOB Analysis - Onhirun, Nippitch; Charoenporn, Pattama (My University, 2022)
In this research, experiments were conducted on the problem of image analysis in atherosclerotic disease. The study was conducted from intravascular ultrasound images that have been taken inside the blood vessels from patients with arterial wall hardening or stenosis. These images are used by experts or specialists to analyze the problem of the disease. There may be some interference from shadows or elements that occurred while taking the image that may cause the incomplete image. We propose an applied method by using computer techniques that help to analyze the components within the image to find the area of interest by enhancement image. Then, the pixel levels are analyzed and classified to divide the background from the image. Next, the morphological operation is used to readjust image properties, and blob analysis is used to identify the region of interesting pixel values. In the final process, Connected Component Analysis (CCA) was used to detect media-adventitia and lumen boundary area The efficacy of segment results was measured by comparing with the expert to measure performance as (media-adventitia, lumen) the Jaccard Index = (0.9570, 0.96695), Hausdorff Distance = (0.5259, 0.6304), Percentage Area Distance = (0.0382, 0.03395) IVUS dataset was used from Simone Balocco, Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain.
Keywords: Atherosclerotic disease; Blob analysis; IVUS image; Segmentation