Abstract
Lung cancer is a prevalent cancer that needs early diagnosis because of its deadly repercussion. Computer systems with certain image processing techniques should be used to increase the speed and accuracy of lung cancer detection. Because of high resolution and low noise, CT images are normally used to process medical images. However, the inevitable noise is introduced in CT images, because of uncertain statistics in physical measurements in CT images. In general, speckle noise, Gaussian noise, and salt and pepper noise occurred primarily in MRI, CT scan, and ultrasound images. In this article, we compared the performance of two segmentation algorithms, the region growing algorithm with the combination of Watershed and Active Contour algorithms, which are more applicable and efficient against the speckle noise and Gaussian noise in medical CT images. Results show that the proposed approach is more effective in segmentation of lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.
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Mousavi, M., Shariaty, F., Orooji, M., Velichko, E. (2021). The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans. In: Velichko, E., Vinnichenko, M., Kapralova, V., Koucheryavy, Y. (eds) International Youth Conference on Electronics, Telecommunications and Information Technologies. Springer Proceedings in Physics, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-58868-7_63
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