Transition region based approach for skin lesion segmentation

Parida, Priyadarsan; Rout, Ranjita

doi:10.5565/rev/elcvia.1177

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The thresholding algorithm uses a threshold value that functions to separate the foreground/object from the background so that each area does not cover the other. Image pixels that have a value that exceeds or less than the threshold value, and then the pixels will be grouped into a predefined area [31]. Formula ( 2) is an equation used to convert grayscale to binary with the thresholding algorithm [32].…”

Section: Convert To Binary Imagementioning

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.

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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: Convert To Binary Imagementioning

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.

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Image Analysis and Diagnosis of Skin Diseases - A Review

Zhao

et al. 2023

CMIR

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Background: Skin disease image analysis has drawn extensive attention from researchers, which can help doctors efficiently diagnose skin disease from medical images. Existing reviews have focused only on the specific task of skin disease diagnosis based on a single medical image type. Discussion: This paper presents the latest and comprehensive review of image analysis methods in skin diseases, and summarizes over 350 contributions to the field, most of which appeared in the last three years. We first sort out representative publicly available skin datasets and summarize their characteristics. Thereafter, aiming at the typical problems exposed by datasets, we organize the image preprocessing and data enhancement part. Further, we review the single tasks of skin disease image analysis in the literature such as classification, detection or segmentation, and analyze the improvement direction of their corresponding methods. Additionally, popular multi-task models based on structure and loss function are also investigated. Conclusions: Challenges involved from the aspects of the dataset and model structure are discussed.

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Transition region based approach for skin lesion segmentation

Cited by 2 publications

References 24 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

Image Analysis and Diagnosis of Skin Diseases - A Review

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