Image Segmentation using Color Value of the Hue in CT Scan Result

Abstract

Object recognition with visual computer techniques is a process necessary to detect different objects in a three-dimensional image to get precise results. Color extraction process Red Green Blue (RGB), Red, Green, Blue (HSV) and other color spaces can produce high accuracy and minimize the number of parameters to speed the computing process. In this study the diagnosis of pneumonia was based on a CT scan of the lungs in the color image segmentation process. The removal of color features is done by using the HSV color segmentation method to classify the pneumonia in CT scans, which makes it possible to detect an image segmenting object as a blob. The color sample generates a value that is the reference point for the filter range during the segmentation process, based on the test results. In this process, it is clear how many objects are found as a color-based pneumonia diagnosis by changing the HSV and RGB thresholds.

Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques

“…Wang et al [18] designed a refinement module to optimise the details of underwater images, achieving better visual effects and reporting the effectiveness of the proposed UIE-Convformer. In the realm of medical diagnosis, Simbolon et al [19] employed colour segmentation in CT scans for pneumonia detection, demonstrating the significant potential of visual computing techniques in healthcare applications. Garaba et al's [20] study on the effectiveness of the Forel-Ule Colour Index (FUI) scale in assessing natural water properties revealed its versatility in measuring water quality through ocean colour remote sensing products, handheld scales, and mobile apps.…”