Image Processing Techniques on Radiological Images of Human Lungs Effected by COVID-19

A, Sirisha; Venkateswararao, Pinagadi

doi:10.30630/joiv.4.2.359

Cited by 6 publications

(4 citation statements)

References 12 publications

(16 reference statements)

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“…Where the variable symbol used is the same as equation (10). A process illustration of subtraction is presented in Fig.…”

Section: J Image Subtractionmentioning

confidence: 99%

“…The main drawback of this CPR is that it takes a long time, so it is inadequate with the number of sufferers [8]. Meanwhile, to identify thoracic damage, a scan or imaging of the thorax must be carried out using a Computed Tomography CT-Scan [9], because there is a significant difference between images of healthy lungs and lungs of COVID-19 sufferers [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Na’am¹,

Pranata²,

Hidayat³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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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.

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“…Where the variable symbol used is the same as equation (10). A process illustration of subtraction is presented in Fig.…”

Section: J Image Subtractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Na’am¹,

Pranata²,

Hidayat³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, fundus images are usually recorded in low contrast. The images in the green channel were preprocessed with double top hat and bottom hat filtering [25]. The visuals get clearer as a result, and the abnormalities in the retina image become more visible [26].…”

Section: A Image Processing Algorithmmentioning

confidence: 99%

Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

Toresa

Shahril

Harun

et al. 2021

JOIV : Int. J. Inform. Visualization

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Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.

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“…This DIP starts with using camera sensors for sample data collection, which will be processed to obtain a segmentation of the sample body. The segmentation is generally obtained from analysis in the form of one of two basic properties, i.e., the value of intensity, discontinuity, and image similarity to the surrounding environment [12], so that the pixel value representing the sample height is obtained. Furthermore, it is compared with the actual height value using four regression analysis methods to obtain the best equation to represent the actual height.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

Abadi

Tahcfulloh²

2022

JOIV : Int. J. Inform. Visualization

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Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.

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Image Processing Techniques on Radiological Images of Human Lungs Effected by COVID-19

Cited by 6 publications

References 12 publications

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

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

Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

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