Segmentation Stroke Objects based on CT Scan Image using Thresholding Method

Badriyah, Tessy; Sakinah, Nur; Syarif, Iwan; Syarif, Daisy Rahmania

doi:10.1109/stud49732.2019.9018825

Cited by 15 publications

(8 citation statements)

References 1 publication

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“…The fitness function for the second image is 556,084 using the hill climbing and 549,719 using the Bat algorithm, where the lower value of the function indicates an optimal threshold. Precision (P), recall (R), and F-score in ( 15), (16), and ( 17) were calculated to assess the proposed work performance. As a first step, we use the output image and the ground truth image to calculate the confusion matrix.…”

Section: Resultsmentioning

confidence: 99%

“…This technique is based on segmentation and dimensionality decrease using principal component analysis, Otsu thresholding, threshold combination, and final thresholding. Meanwhile, brain image segmentation using Otsu thresholding is proposed by Badriyah et al [16] to determine the characteristics of a specific stroke kind. They evaluated the results using the peak signal-to-noise ratio and mean-square error.…”

Section: Introductionmentioning

confidence: 99%

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An Otsu thresholding for images based on a nature-inspired optimization algorithm

Raheem¹,

Shabat²

2023

IJEECS

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Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Otsu thresholding for images based on a nature-inspired optimization algorithm

Raheem¹,

Shabat²

2023

IJEECS

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“…The use of noise and filters is based on measuring image quality values. There are four image quality measurements, namely Mean Square Error (MSE) [12]- [13], Peak Signal Noise Ratio (PSNR) [14]- [16], Variation of Information (VOI) [17]- [18], and Global Consistency Error (GCE) [19]- [21]. The smaller the MSE value, the better the recovery value from the noise effect, and in PSNR the higher the value, the better because it is considered close to the original image.…”

Section: ) Noise Repairmentioning

confidence: 99%

Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm

Furizal,

Mawarni,

Akbar

et al. 2023

Control Syst. Optim. Lett.

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This study underscores the importance of wound image segmentation in the medical world to speed up first aid for victims and increase the efficiency of medical personnel in providing appropriate treatment. Although the body has a protective function from external threats, the skin can be easily damaged and cause injuries that require rapid detection and treatment. This study used the K-Means clustering algorithm to segment the external wound image dataset consisting of three types of wounds, namely abrasion, puncture, and laceration. The results showed that K-Means clustering is an effective method for segmenting wound images. The greater the number of segments used, the better the quality of the resulting segmentation. However, it is necessary to take into account the specific characteristics of each type of wound and the number of segments used in order to choose the most suitable segmentation method. Evaluation using various metrics, such as VOI, GCE, MSE, and PSNR, provides an objective assessment of the quality of segmentation. The results showed that abrasion wounds were easier to segment compared to puncture wounds and lacerations. In addition, the size of the image file also affects the speed of program execution, although it is not always absolute depending on the characteristics of the image.

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“…Badriyah et al [91] discussed the concept of stroke segmentation using the thresholding technique. They found out that this technique needs optimization to remove the brain skull image so that the cerebral scalp is invisible and sharpens the edges of the brain.…”

Section: Threshold Based Segmentationmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

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Segmentation Stroke Objects based on CT Scan Image using Thresholding Method

Cited by 15 publications

References 1 publication

An Otsu thresholding for images based on a nature-inspired optimization algorithm

An Otsu thresholding for images based on a nature-inspired optimization algorithm

Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm

Brain stroke computed tomography images analysis using image processing: A Review

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