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Numer specjalny 3/2024 vol. 57
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PRACA POGLĄDOWA
Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases
Zbigniew Orzeł 1
,
 
Amelia Kosior-Romanowska 2
,
 
Piotr Bednarczuk 1
 
 
 
Więcej
Ukryj
1
WSEI University
 
2
Netrix S.A.
 
 
Data nadesłania: 21-06-2024
 
 
Data akceptacji: 17-07-2024
 
 
Data publikacji: 20-08-2024
 
 
Autor do korespondencji
Zbigniew Orzeł   

WSEI University
 
 
JoMS 2024;57(Numer specjalny 3):609-621
DOI: https://doi.org/10.13166/jms/191311
 
 
Artykuł (PDF)
Referencje (28)
 
SŁOWA KLUCZOWE
Medical imaging
CT data analysis
Segmentation algorithms
Efficiency of diagnosis
Thresholding with maximum entropy
 
DZIEDZINY
_Inne
 
STRESZCZENIE
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.
Licencja
Ta praca jest dostępna na licencji Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA)
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