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This work proposes a novel method for automatic detection of COVID-19 infection in CT images. COVID-19 has been a devastating infectious disease that ravaged the global community for last several months. Even though there are ongoing efforts to rapidly make all COVID-19 information and resources freely available to global community, availability of large amount of labeled data for disease diagnosis, prognosis and prediction is still a challenging task. Many of the recently published studies on detection of COVID-19 patients using deep learning methods do not have adequate dataset for robust training, validation, and testing. Therefore, this work develops a computational image analysis method that uses small amount of COVID-19 CT images for infection detection in CT images. The proposed method generates lung mask by segmenting the area of lung, after removing isolated high intensity areas from the surrounding ribs in the input CT image by employing an optimal segmentation algorithm and morphological operations. Next, the proposed method employs the segmented lung mask to detect areas of infection in the lung. To distinguish between disease and non-disease areas in the lung, the variance of the gray-level of each region is computed and used as feature to detect region of disease which are distinguished by low variance compare to Non-COVID-19 regions. The performance of proposed scheme is evaluated on a dataset of 72 CT-images (36 Non-Covid-19 images and 36 Covid-19 images) with ground-truth generated by radiologists. The accuracy of detection for Covid-19 images is 91.7% and that for Non-Covid-19 images is 91.7%, respectively. The proposed algorithm cannot distinguish between COVID-19 disease and other disorder in lung of patient.
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