Paper Info
Title
Classification of cirrhotic liver in Gadolinium-enhanced MR images
Abstract
Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns. Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regionsof-interest (ROls). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers. Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice. This is because the 'true disease states' of the ROls are required for the training of the classifier but is, generally, not available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROls in that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic livers.
Year
DOI
Venue
2007
10.1117/12.710288
Proceedings of SPIE
Keywords
Field
DocType
computer-aided diagnosis (CAD),cirrhosis of the liver,magnetic resonance imaging (MRI),k-means clustering
k-means clustering,Cirrhosis,Fibrosis,Radiology,Classifier (linguistics),Cluster analysis,Medicine,Gadolinium
Conference
Volume
ISSN
Citations 
6514
0277-786X
1
PageRank 
References 
Authors
0.35
8
10
Name
Order
Citations
PageRank
Gobert Lee1112.76
Yoshikazu Uchiyama2699.58
Xuejun Zhang37016.55
Masayuki Kanematsu49017.09
Xiangrong Zhou532545.53
Takeshi Hara663979.10
Hiroki Kato742.09
Hiroshi Kondo810.35
Hiroshi Fujita910.35
Hiroaki Hoshi1010618.21