Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases. The article aims to review selected methods for segmenting lung medical images based on computed tomography (CT) image data analysis. Included in the analysis are 11 different approaches, includi...

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Vydáno v:Journal of Modern Science Ročník 57; číslo 3; s. 609 - 621
Hlavní autoři: Orzeł, Zbigniew, Kosior-Romanowska, Amelia, Bednarczuk, Piotr
Médium: Journal Article
Jazyk:angličtina
Vydáno: Wydawnictwo Akademii Nauk Stosowanych WSGE im. A. De Gasperi w Józefowie 2024
WSGE University of Applied Sciences in Józefów Publishing House
Akademia Nauk Stosowanych WSGE im. A. De Gasperi w Józefowie
Témata:
ct data analysis
Economy
efficiency of diagnosis
medical imaging
segmentation algorithms
thresholding with maximum entropy
Medical imaging
CT data analysis
Thresholding with maximum entropy
Segmentation algorithms
Efficiency of diagnosis
ISSN:1734-2031, 2391-789X
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Shrnutí:Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases. The article aims to review selected methods for segmenting lung medical images based on computed tomography (CT) image data analysis. Included in the analysis are 11 different approaches, including edge algorithms such as Canny, Prewitt, Roberts, Sobel and Log, clustering-based algorithms such as Fuzzy C-means, genetic K-means, k-nearest neighbours, and neural networks such as perceptron, neural and fuzzy sets, and edge binarization techniques, among others. Each method was evaluated for its ability to segment lung structures on CT images accurately. Finally, the contour of the heart from the CT image was determined using the maximum entropy thresholding method. The juxtaposition of different approaches to medical image segmentation is an important contribution to developing medical diagnostic techniques, which can help improve the efficiency of diagnosing lung diseases.
ISSN:1734-2031
2391-789X
DOI:10.13166/jms/191311