Image thresholding approaches for medical image segmentation - short literature review

Jardim, Sandra; António, João; Mora, Carmen de

doi:10.1016/j.procs.2023.01.439

Cited by 23 publications

(10 citation statements)

References 30 publications

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“…The lung segmentation steps represent in Fig. 5 as follows: Thresholding, a widely used method for segmenting regions [40] , involves dividing an image into sets of pixels with values that are either lower or higher than the specified threshold. In this thresholding step, the grayscale image with HU scale is segmented to obtain the lung field area using a threshold value of Hounsfield Unit (HU) < −300.…”

Section: Proposed Methodsmentioning

confidence: 99%

Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

Hermawati,

Trilaksono,

Nugroho

et al. 2024

MethodsX

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Section: Proposed Methodsmentioning

confidence: 99%

Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

Hermawati,

Trilaksono,

Nugroho

et al. 2024

MethodsX

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“…Traditional segmentation methods have historically served as the building blocks of medical image analysis [32,34]. These methods include thresholding, region growing, and edge detection [35][36][37]. Researchers conducted a comparative study of thresholding techniques for medical image segmentation [34,38] and highlighted their simplicity and limitations in handling complex intensity variations [39].…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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“…On the other hand, the thresholding technique is a simple and widely used method in medical image segmentation [30,31]. This involves setting a threshold value to distinguish between foreground and background pixels based on intensity or other image characteristics.…”

Section: Used Methodologies In Automatic Keyframe Extraction May Vary...mentioning

confidence: 99%

Deep AngioKey: A Novel Approach for Objective Keyframe Extraction in Coronary Angiography Analysis

Moalla,

Ghrab,

Bahloul

et al. 2023

Preprint

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Coronary angiography represents the GOLD standard for diagnosing and treating obstructive coronary artery disease. Its analysis relies on cardiologist?s visual assessment which is subjective and leads to inter-observer variability and different treatment strategies. Objective methods have been proposed and implemented in catheterization laboratories. However, their input is manually chosen by the cardi-ologist. In this paper, we present a new coronary angiogram keyframe extraction, Deep AngioKey. It is based on feature extraction. The frame with the highest vessel structure was identified as keyframe. Feature extraction was done using a U-Net model trained for binary vessel segmentation. Our solution surpassed existing methods and reached an overall accuracy of 98.1% in keyframe identification with an average main frame distance of 1.63.

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Image thresholding approaches for medical image segmentation - short literature review

Cited by 23 publications

References 30 publications

Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Deep AngioKey: A Novel Approach for Objective Keyframe Extraction in Coronary Angiography Analysis

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