Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition

Leticia Rittner; Jayaram K. Udupa; Drew A. Torigian

doi:10.1117/12.2044297

21 March 2014 Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition

Leticia Rittner, Jayaram K. Udupa, Drew A. Torigian

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90342U (2014) https://doi.org/10.1117/12.2044297
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Computerized automatic anatomy recognition (AAR) is an essential step for implementing body-wide quantitative radiology (QR). Our strategy to automatically identify and delineate various organs in a given body region is based on fuzzy models and an organ hierarchy. In previous years, the basic algorithms of our AAR approach - model building, recognition, and delineation - and their evaluation were presented. In the present paper, we propose to replace the single fuzzy model built for each organ by a set of fuzzy models built for the same organ. Based on a dataset composed of CT images of the Thorax region of 50 subjects, our experiments indicate that recognition performance improves when using multiple models instead of a single model for each organ. It is interesting to point out that the improvement is not uniform for all organs, leading us to conclude that some organs will benefit from the multiple model approach more than others.

Citation Download Citation

Leticia Rittner, Jayaram K. Udupa, and Drew A. Torigian "Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342U (21 March 2014); https://doi.org/10.1117/12.2044297

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