It is expected automatic colon segmentation will improve the radiologists’diagnostic performance.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, an automatic colon segmentation method for Computed Tomography (CT)colonography is presented.
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Key Findings & Data:Colon segmentation is considered in order to prevent the timeconsumption while searching polyps out of the colon region and reduce radiologists’ interpretationtime.
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Conclusion & Impact:It is expected automatic colon segmentation will improve the radiologists’diagnostic performance.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper, an automatic colon segmentation method for Computed Tomography (CT)colonography is presented. Colon segmentation is considered in order to prevent the timeconsumption while searching polyps out of the colon region and reduce radiologists’ interpretationtime. The proposed method is the combination of pre-processing and Cellular Neural Networks(CNN). Also recurrent perceptron learning algorithm (RPLA) is used for CNN training. Original CTimages are passed through a threshold and then CNN is used to erase unrelated small objects andsmooth sharp corners. It is expected automatic colon segmentation will improve the radiologists’diagnostic performance.
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Cite This Article
KILIC, N., UCAN, O. N., DEMIREL, K., OSMAN, O. (2022). Automatic colon segmentation using cellular neural network for colorectal polyps detection. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 7 - Sayı: 2, 419–422. https://ajindex.com/articles/543787
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