Image-guided decision support system for pathology

We present a content-based image retrieval system that supports decision making in clinical pathology. The image-guided decision support system locates, retrieves, and displays cases which exhibit morphological profiles consistent to the case in question. It uses an image database containing 261 digitized specimens which belong to three classes of lymphoproliferative disorders and a class of healthy leukocytes. The reliability of the central module, the fast color segmenter, makes possible unsupervised on-line analysis of the query image and extraction of the features of interest: shape, area, and texture of the nucleus. The nuclear shape is characterized through similarity invariant Fourier descriptors, while the texture analysis is based on a multiresolution simultaneous autoregressive model. The system performance was assessed through ten-fold cross-validated classification and compared with that of a human expert. To facilitate a natural man-machine interface, speech recognition and voice feedback are integrated. Client-server communication is multithreaded, Internet-based, and provides access to supporting clinical records and video databases.