Image mining for investigative pathology using optimized feature extraction and data fusion.

Chen W(1), Meer P(3), Georgescu B(4), He W(1), Goodell LA(2), Foran DJ.(1,2,3)

(1) Center for Biomedical Imaging & Informatics
(2) Department of Pathology & Laboratory Medicine
University of Medicine & Dentistry of New Jersey
Piscataway, NJ 08854

(3) Department of Electrical and Computer Engineering
Center for Advanced Information Processing
Rutgers University
Piscataway, NJ 08854

(4) Siemens Corporate Research
Integrated Data Systems Department
Princeton, NJ

In many subspecialties of pathology, the intrinsic complexity of rendering accurate diagnostic decisions is compounded by a lack of definitive criteria for detecting and characterizing diseases and their corresponding histological features. In some cases, there exists a striking disparity between the diagnoses rendered by recognized authorities and those provided by non-experts. We previously reported the development of an Image Guided Decision Support (IGDS) system, which was shown to reliably discriminate among malignant lymphomas and leukemia that are sometimes confused with one another during routine microscopic evaluation. As an extension of those efforts, we report here a web-based intelligent archiving subsystem that can automatically detect, image, and index new cells into distributed ground-truth databases. Systematic experiments showed that through the use of robust texture descriptors and density estimation based fusion the reliability and performance of the governing classifications of the system were improved significantly while simultaneously reducing the dimensionality of the feature space.

Computer Methods and Programs in Biomedicine , 79, 59-72, 2005.

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