A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images

Authors:
Dimitris Glotsos;Ioannis Kalatzis;Pantelis Theocharakis;Pantelis Georgiadis;Antonis Daskalakis;Kostas Ninos;Pavlos Zoumboulis;Anna Filippidou;Dionisis Cavouras
Affiliations:
Department of Medical Instruments Technology, Technological Educational Institute of Athens, Ag. Spyridonos, Aigaleo, Athens 12210, Greece;Department of Medical Instruments Technology, Technological Educational Institute of Athens, Ag. Spyridonos, Aigaleo, Athens 12210, Greece;Medical Image Processing and Analysis Laboratory, Department of Medical Physics, University of Patras, Rio-Patras 26500, Greece;Medical Image Processing and Analysis Laboratory, Department of Medical Physics, University of Patras, Rio-Patras 26500, Greece;Medical Image Processing and Analysis Laboratory, Department of Medical Physics, University of Patras, Rio-Patras 26500, Greece;Department of Medical Instruments Technology, Technological Educational Institute of Athens, Ag. Spyridonos, Aigaleo, Athens 12210, Greece;Ultrasound Department, Echonet, 319 Kifissias Avenue, Kifissia, Athens 14561, Greece;Ultrasound Department, Echonet, 319 Kifissias Avenue, Kifissia, Athens 14561, Greece;Department of Medical Instruments Technology, Technological Educational Institute of Athens, Ag. Spyridonos, Aigaleo, Athens 12210, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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Abstract

A computer-aided diagnostic system has been developed for the discrimination of normal, infectious and cancer prostate tissues based on texture analysis of transrectal ultrasound images. The proposed system has been designed using a panel of three classifiers, which have been evaluated individually or as a mutli-classifier scheme, using the external cross-validation procedure. Clinical data consisted of 165 transrectal ultrasound images, characterized by an experienced physician as normal (55/165), cancerous (55/165), and infectious (55/165) prostate cases. From each image, the physician delineated the most representative regions of interest, from which, 23 textural features were extracted. Classification was seen as a two level hierarchical decision tree. Normal from infectious and infectious from cancer cases were discriminated at the 1st and 2nd level of the decision tree, respectively. The best classification results for the 1st level were 89.5%, whereas for the 2nd level 90.1%. The utilization of multi-classifier system improved the discrimination of prostate pathologies as compared to individual classifiers; for infectious prostate cases improvement was from 87.3% to 88.7% and for cancer prostate cases improvement was from 84.1% to 91.4%. In terms of overall system performance (the decision tree's node propagating error taken into account), best classification accuracies were 89.5%, 79.6% and 82.7% for the recognition of normal, infectious and cancer cases, respectively. The proposed system might be used as a second opinion tool for assisting diagnosis of different prostate pathologies.