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Erschienen in:
2024 | OriginalPaper | Buchkapitel
A Simple and Effective Method for Segmenting Lung Regions from CT Scan Images Using K-Means
verfasst von : Yumnam Kirani Singh
Erschienen in: Big Data, Machine Learning, and Applications
Verlag: Springer Nature Singapore
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Abstract
Proposed here is a simple and effective method for segmenting lung regions from CT-scan image. In this method, the CT-scan image in DICOM format is converted into RGB image, which is then further converted into gray image. The resulted grayscale image is then binarized using K-means, which automatically groups the pixels into two clusters of pixels; one belonging to the background pixels, while the other cluster to the pixels belonging to the lung regions. From the cluster of pixels belonging to the lung regions, the left and right lungs can be properly separated. The K-Means clustering used in the method is based on recursive averaging to avoid overflow errors while computing and updating cluster centers. As compared to other traditional methods of lung region segmentation, it is much simpler and gives better results. Also, it is much simpler and faster than the deep learning methods for lung region segmentation as it does not require any rigorous training with a large number of images.