Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19

Abstract

Ground-Glass Opacity (GGO) is an object found in the thorax due to infection. This object interferes with the normal function of the thorax in breathing. The characteristic of GGO has slightly lighter turbidity compared to normal thorax tissue on radiological images, so it is very difficult to identify it precisely. This study aims to identify the GGO pattern and find the exact area of the CT-scan image of COVID-19 sufferers. The data tested were 34 images from 34 different patients. The image was taken using CT-Scan equipment with the tube model 46274891G1 axially. Each patient is taken one image with the reading position right above the chest using the file format Joint Photographic Experts Group (jpg). An automatic image processing model developed in this study uses several interrelated and continuous technical steps; Image Enhancement, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify GGO in all patient test images, where each patient has GGO. The smallest area of GGO was 3.9%, and the highest was 34.2% of the total thorax area. This level of comparison is greatly influenced by the severity of the COVID-19 virus patient. This area of GGO weakens the normal function of the thorax in the respiratory process of the patient. Thus, this research can be used as a model recommendation in identifying thorax damage due to COVID-19 very well in following up on more intensive treatment in the future.

Investigating the trend of demographic changes, mortality, clinical and paraclinical findings of patients hospitalized in the Corona ward, before and after the start of general vaccination of COVID-19

“…The results of the study of Mamatha Reddy D. Cozzi et al (2021) [42] are in line with this finding of the present study in such a way that the results of their study showed that: before the start of general vaccination against COVID-19, most hospitalized patients had ground-glass opacities (GGO) on CT, but after the vaccination, GGO results decreased. Also, the results of the study of Jufriadif Na'am et al [43] (2021) are in line with this finding of the present study in such a way that the results of their study showed that: before the start of general vaccination against COVID-19, most hospitalized patients had radiology results showing ground glass opacity (GGO) in their thorax.…”

Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques

“…The results of the detection will be tested to measure the severity of the lung condition with the detected GGO object. The measurement process can be carried out using Equation 1 [20].…”

“…The problem in the GGO diagnosis process was initially carried out by presenting fairly good results with an accuracy of 90.83% and an average precision of 0.905 [19]. Furthermore, other studies have also explained that DIP is able to produce a model for identifying lung damage due to COVID-19 based on the description of the GGO pattern [20]. DIP can also be used as a useful tool for clinical practitioners and radiologists in the process of diagnosis, quantification, and follow-up in handling COVID-19 cases [21].…”

Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

“…The results of the image extraction process using the morphological method are then labeled for each matrix area using the Regionprops method. Regionprops detects white objects with a pixel value of 1 and black objects with a pixel value of 0 as the background [29].…”