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Implementing a non-local means method to CTA data of aortic dissection

1Department of Electrical and Computer Engineering, Universitas Syiah Kuala. Jl. Tgk. Syech Abdur Rauf No. 7 Kopelma Darussalam, Banda Aceh 23111, Indonesia

2Department of Intelligent System, Faculty of Engineering, University of Duisburg-Essen. Bismarckstrasse 90, Building BC, 4. Floor, Duisburg 47057, Germany

Received: 3 Mar 2021; Revised: 3 Jun 2021; Accepted: 17 Jun 2021; Available online: 2 Jul 2021; Published: 31 Jul 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.
Keywords: aortic dissection; noise reduction; non-local means, CT image, denoising method;
Funding: Universitas Syiah Kuala, Indonesia;University of Duisburg-Essen, Germany

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