@article {62551,
	title = {Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images},
	journal = {Pattern recognitionPattern Recognition},
	volume = {114},
	year = {2021},
	month = {2021},
	pages = {107747},
	abstract = {
	History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general,\&nbsp;Computer Tomography\&nbsp;(CT) is used to detect tumors in\&nbsp;pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT\&nbsp;imaging system\&nbsp;are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT\&nbsp;image segmentation\&nbsp;is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of\&nbsp;artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art\&nbsp;segmentation approaches, namely GraphCut,\&nbsp;Medical Image Segmentation\&nbsp;(MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and\&nbsp;Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

},
	isbn = {0031-3203},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320320305501},
	author = {OULEFKI, ADEL and Agaian, Sos and Trongtirakul, Thaweesak and Laouar, Azzeddine Kassah}
}
