Recognizing abdominal organs in CT images using contextual neural network and fuzzy rules

This paper describes a method for automatic abdominal organ recognition from a series of CT image slices, that is based on shape analysis, image contextual constraint, and between-slice relationship. A contextual neural network is applied to segment each image slice into disconnected regions. For each region, its shape features are calculated, along with its spatial relationships with respect to spine. Then, according to the knowledge of anatomy, these features are constructed to form fuzzy rules used for organ recognition. In the recognition process, the obtained features and the overlapping information between adjacent slices are used for identifying each organ. This proposed method of recognizing organs has been successfully tested in several clinical cases.