Image mining: issues, framework, a generic tool and its application to medical-image diagnosis

Abstract A tool and a methodology for data mining in picture-archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the data base of image descriptions. Knowledge-engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list and stores descriptions in the data base. Digital-image processing can be applied to improve imaging of specific image features, or to get expert-independent feature evaluation. Decision-tree induction is used to learn the expert knowledge, presented in the form of image descriptions in the data base. A constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented and its application to image mining is shown on the task of Hep-2 cell-image classification. However, the tool and the methodology are generic and can be used for other image-mining tasks. We applied the developed methodology of data mining in other medical tasks, such as in lung-nodule diagnosis in X-ray images, lymph-node diagnosis in MRI and investigation of breast MRI.

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