ROI extraction in CT lung images of COVID-19 using Fast Fuzzy C means clustering

Kumar, S.N.; Ahilan, A.; Fred, A. Lenin; Kumar, H. Ajay

The outbreak of coronavirus is intense in most countries around the world. The Region of Interest (ROI) extraction in medical images plays a vital role in the disease diagnosis and therapeutic planning. Clustering is extensively used in data mining applications for the grouping of data. The CT medical imaging modality is one of the diagnostic tools for COVID-19 and as a primary screening tool prior to the confirmation by reverse-transcription polymerase chain reaction (RT-PCR) lab testing. The FCM algorithm gains importance in medical image processing for the segmentation of anomalies. This research work proposes Fast Fuzzy C means clustering for the ROI extraction in CT lung images of Coronavirus Pneumonia. Prior to the segmentation, preprocessing was performed by median filter. The validation of fast FCM was done by partition coefficient and partition entropy. The computation complexity of Fast Fuzzy C means algorithm was low, when compared with the classical FCM algorithm.