Detecting Prostatic Adenocarcinoma from Digitized Histology Using a Multi-Scale, Hierarchical Classification Approach

TitleDetecting Prostatic Adenocarcinoma from Digitized Histology Using a Multi-Scale, Hierarchical Classification Approach
Publication TypeConference Paper
Year of Publication2006
AuthorsDoyle, S, Rodriguez C, Madabhushi A, Tomaszewski JE, Feldman MD
Conference NameIEEE International Conference of Engineering in Medicine and Biology Society (EMBS)
ISBN Number1557-170X (Print)1557-170X (Linking)
Accession Number17947116
KeywordsAdenocarcinoma/classification/*diagnosis/*pathology, Algorithms, Automation, Biopsy, Decision Support Techniques, Diagnosis, Computer-Assisted/*instrumentation/methods, Humans, Image Processing, Computer-Assisted, Male, Models, Statistical, Models, Theoretical, Pattern Recognition, Automated, Prostatic Neoplasms/classification/*diagnosis/*pathology, Reproducibility of Results, Software

In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using a hierarchical classifier within a multi-scale framework. Pyramidal decomposition is used to reduce an image into its constituent scales. The cascaded image analysis across multiple scales is similar to the manner in which pathologists analyze histopathology. Nearly 600 different image texture features at multiple orientations are extracted at every pixel at each image scale. At each image scale the classifier only analyzes those image pixels that have been determined to be tumor at the preceding lower scale. Results of quantitative evaluation on 20 patient studies indicate (1) an overall accuracy of over 90% and (2) an approximate 8-fold savings in terms of computational time. Both the AdaBoost and Decision Tree classifiers were considered and in both cases tumor detection sensitivity was found to be relatively constant across different scales. Detection specificity was however found to increase at higher scales reflecting the availability of additional discriminatory information.

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