A method for linking computed image features to histological semantics in neuropathology
Journal of Biomedical Informatics
Marrow cell segmentation by simulating visual system
ICNC'09 Proceedings of the 5th international conference on Natural computation
Automatic classification of lymphoma images with transform-based global features
IEEE Transactions on Information Technology in Biomedicine
Classification of malignant lymphomas by classifier ensemble with multiple texture features
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
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The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in Java, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a gold standard database which exhibit spectral and spatial profiles which are most similar to a given query image.