ITM Web Conf.
Volume 56, 2023First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|Number of page(s)||8|
|Section||Language & Image Processing|
|Published online||09 August 2023|
Comparative Analysis Between Different Lung Segmentation Techniques
1,3 – 6 Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India
2 Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, India
* Corresponding author: email@example.com
For the purpose of identifying various lung illnesses, computed tomography (CT) pictures of the lung must be segmented. The most significant aspect of medical imaging is image segmentation. Via an automated process, the ROI (region of interest) is extracted. The process of segmentation separates an image into sections according to a particular interest, such as segmenting human organs or tissue. Several medical disorders can benefit from the segmented image of the lung. We specifically compared and analysed various threshold segmentation algorithms in this paper in an effort to determine which one would be the best to use moving forward with image processing. We have used Computed Tomography (CT) images of Lungs with Tuberculosis (TB) dataset from Kaggle for image processing and compared them with finely masked CT images to infer the best Threshold algorithm. We have decided to do the analysis on Threshold algorithms named as Binary Threshold, Otsu’s Threshold, and Adaptive Threshold. Comparison has been done based on performance parameters such as Accuracy, Precision, Recall Value, f1-score, etc. The results are also represented in Graphical format for better understanding of performed comparison study.
Key words: Image Processing / Python / Comparative Data Analysis / Adaptive Threshold / Otsu Threshold / Binary Threshold
© The Authors, published by EDP Sciences, 2023
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