Article: Automated lesion segmentation of COVID-19 chest CT scan images using magneto-static active counter model Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.17 No.3 pp.362 - 374 Abstract: The novel coronavirus disease, often referred to as COVID-19, is an airborne infection that is very infectious and has been responsible for a substantial amount of harm all over the globe. It got its name from the COVID-19 virus that caused it. The recent years have witnessed the appearance of more COVID-19 varieties, which has made the current scenario a great deal more complex and possibly harmful. This is due to the fact that the situation has become much more complicated. Evaluation and measurement of COVID-19 chest anomalies may be able to be of assistance in all of these areas. In this investigation, we make use of CT images to test the applicability of magneto-static active contour models (MACMs) for the segmentation of pneumonia caused by COVID-19, which is a disease caused by the coronavirus. MCMs are a productive method for image segmentation. The proposed way for adding more data to the training set can raise the average DSC from 0.7599 to 0.781. This method can drop the average MAE from 0.0074 to 0.0067 and raise the average SEN from 0.8104 to 0.8308. Inderscience Publishers - linking academia, business and industry through research

Title: Automated lesion segmentation of COVID-19 chest CT scan images using magneto-static active counter model

Authors: S. Salini; B. Selvapriya

Addresses: Department of Computer Science and Engineering, Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, 173, Agaram Main Rd., Selaiyur, Chennai, Tamil Nadu 600073, India ' Department of Computer Science and Engineering, Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, 173, Agaram Main Rd., Selaiyur, Chennai, Tamil Nadu 600073, India

Abstract: The novel coronavirus disease, often referred to as COVID-19, is an airborne infection that is very infectious and has been responsible for a substantial amount of harm all over the globe. It got its name from the COVID-19 virus that caused it. The recent years have witnessed the appearance of more COVID-19 varieties, which has made the current scenario a great deal more complex and possibly harmful. This is due to the fact that the situation has become much more complicated. Evaluation and measurement of COVID-19 chest anomalies may be able to be of assistance in all of these areas. In this investigation, we make use of CT images to test the applicability of magneto-static active contour models (MACMs) for the segmentation of pneumonia caused by COVID-19, which is a disease caused by the coronavirus. MCMs are a productive method for image segmentation. The proposed way for adding more data to the training set can raise the average DSC from 0.7599 to 0.781. This method can drop the average MAE from 0.0074 to 0.0067 and raise the average SEN from 0.8104 to 0.8308.

Keywords: chest CT scans; COVID-19 infection; active contour models; level set methods; region-based models; edge-based models.

DOI: 10.1504/IJCVR.2026.156215

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.3, pp.362 - 374

Received: 29 Jan 2024
Accepted: 07 May 2024
Published online: 08 Sep 2026 *

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Title: Automated lesion segmentation of COVID-19 chest CT scan images using magneto-static active counter model

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