Bimodal system for interactive indexing and retrieval of pathology images
The prototype of a system to assist the physicians in differential diagnosis of lymphoproliferative disorders of blood cells from digitized specimens is presented. The user selects the region of interest (ROI) in the image which is then analyzed with a fast, robust color segmenter. Queries in a database of validated cases can be formulated in terms of shape (similarity invariant Fourier descriptors), texture (multiresolution simultaneous autoregressive model), color (L*u*/spl upsi/* space), and area, derived from the delineated ROI. The uncertainty of the segmentation process (obtained through a numerical method) determines the accuracy of shape description (number of Fourier harmonics). Ten-fold cross-validated classification over a database of 261 color 640/spl times/480 images was implemented to assess the system performance. The ground truth was obtained through immunophenotyping by flow cytometry. To provide a natural man-machine interface, most input commands are bimodal: either using the mouse or by voice. A speech synthesizer provides feedback to the user. All the employed computational modules are context independent and thus the same system can be used in a large variety of application domains.