Processing Classification

Very few methodologies have been developed for the classification of pancreatic tumors, e.g., the differentiation between benign and malignant disease or even the differentiation between normal and abnormal pancreas or pancreatic areas reported. One application used several classification schemes to differentiate between pancreatic ductal adenocarcinoma and mass-forming pancreatitis. The methods included artificial neural network classifiers, Bayesian analysis, and Hayashi's quantification method II [45]. The approach used radiologist-extracted CT features for the classification and no automatic segmentation or feature identification was performed. Results indicated that all computer techniques performed similarly to expert radiologists and had no significant benefits [45]. The classification task adds another level of difficult to the segmentation. It is reasonable to hypothesize that classification my be successful if automated feature extraction is performed or when image and nonimage features are merged in the feature set.

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