Enhancement on Enlarge Image for Identification Lumbar Radiculopathy at Magnetic Resonance Imaging

Yunus, Yuhandri; Harlan, Johan; Santony, Julius; Hidayat, Rahmat; Na’am, Jufriadif

doi:10.18421/tem92-30

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…At this stage, image contrast enhancement is needed in identifying objects in medical images [26]. The algorithm used in this model is Contrast Limited Adaptive Histogram Equalization (CLAHE).…”

Section: B Image Enhancementmentioning

confidence: 99%

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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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Section: B Image Enhancementmentioning

confidence: 99%

“…Five models, namely AlexNet, VGG16, VGG19, GoogleNet, and ResNet50 on the CNN Method, can detect COVID-19 [24]. Deep [26]. So, in this study, a new method is proposed that can be used to automatically identify the patterns and extent of GGO on chest CT scans of COVID-19 sufferers.…”

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.

Self Cite

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show abstract

“…Modalitas pencitraan gigi terutama dikategorikan sebagai sinar-X intraoral dan ekstra-oral. Dalam kedokteran gigi, Citra-citra ini sering digunakan untuk diagnosis medis [28,29]. Berbagai kategorisasi modalitas pencitraan gigi berdasarkan intra-oral dan ekstra-oral disajikan pada Gambar 1.…”

Section: Pendahuluanunclassified

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

Sumijan

2022

komtekinfo

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Many of us as Muslims do not know how to act when the call to prayer is heard, which turns out to have a very noble virtue. This animation is expected to provide insight for all of us about the virtues of answering the call to prayer. In addition, this animation can also attract and entertain the audience. Technological advances that are growing rapidly today cover all fields. One of them is the field of multimedia such as animation. This study aims to create 3D animation that is used as a learning medium. The application has the theme of etiquette when calling the call to prayer. 3D animation is the process of placing objects and characters in three-dimensional space and manipulating them to create the illusion of motion. Design in application development using Blender software and applications in making animation. The results of the resulting application present an interactive form of teaching for users in providing knowledge about manners when the call to prayer is heard. With this application, it is hoped that it can provide an alternative to provide a form of teaching, especially for children to explain etiquette when calling the call to prayer

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“…algorithms generally based on one of the two basic properties of intensity values, discontinuity and similarity [27]. Watershedbased segmentation is a technique in which the entire image is separated into discrete nonoverlapping sections using the watershed concept.…”

Section: ) Segmentation Using Watershed: Image Segmentationmentioning

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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Enhancement on Enlarge Image for Identification Lumbar Radiculopathy at Magnetic Resonance Imaging

Cited by 5 publications

References 27 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

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

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

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