RT Journal Article
SR Electronic
T1 How Efficient is the k-means Clustering to Analyze the CT images of Pyogenic and Amoebic Liver Abscess?
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.06.503068
DO 10.1101/2022.08.06.503068
A1 Subhagata Chattopadhyay
YR 2022
UL http://biorxiv.org/content/early/2022/08/11/2022.08.06.503068.abstract
AB Liver abscesses are well-delineated pus-filled lesions. Two common types are Amoebic liver abscesses (ALA), caused by protozoa called entamoeba histolytica while several pus-forming bacteria cause pyogenic liver abscesses (PLA). Both cause debilitating morbidities and are diagnosed by pus culture-sensitivity tests. A contrast CT abdomen shows well-demarcated lesions in the liver. The telemedicine practice is on the rise where image processing is becoming a part and parcel of teleradiology to fill the gap between the number of radiologists versus the large patient pool. Cluster-based image segmentation is a useful step in grouping the image into the desired number of clusters. The k-means clustering (k-MC) technique is one popular method, used in this study on ALA and PLA contrast CT images. it observes that with the desired 2-clusters – a) normal liver tissue and b) the pus-filled tissue) parameters, the algorithm gives better results in PLA.Competing Interest StatementThe authors have declared no competing interest.ALAAmoebic liver abscessCTComputerized tomographyk-MCk-Means clusteringPLAPyogenic liver abscessRBCRed Blood CellsWBCWhite blood cells