Dr. Roland Kwitt Profile

Dr. Roland Kwitt

at Kitware Inc

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Proceedings Article | 21 March 2014 Paper

Variability sensitivity of dynamic texture based recognition in clinical CT data

Roland Kwitt, Sharif Razzaque, Jeffrey Lowell, Stephen Aylward

Proceedings Volume 9034, 903420 (2014) https://doi.org/10.1117/12.2043271

KEYWORDS: Databases, Kidney, Data modeling, Video, Ultrasonography, Computed tomography, Data acquisition, Transducers, Performance modeling, Medical imaging

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Dynamic texture recognition using a database of template models has recently shown promising results for the task of localizing anatomical structures in Ultrasound video. In order to understand its clinical value, it is imperative to study the sensitivity with respect to inter-patient variability as well as sensitivity to acquisition parameters such as Ultrasound probe angle. Fully addressing patient and acquisition variability issues, however, would require a large database of clinical Ultrasound from many patients, acquired in a multitude of controlled conditions, e.g., using a tracked transducer. Since such data is not readily attainable, we advocate an alternative evaluation strategy using abdominal CT data as a surrogate. In this paper, we describe how to replicate Ultrasound variabilities by extracting subvolumes from CT and interpreting the image material as an ordered sequence of video frames. Utilizing this technique, and based on a database of abdominal CT from 45 patients, we report recognition results on an organ (kidney) recognition task, where we try to discriminate kidney subvolumes/videos from a collection of randomly sampled negative instances. We demonstrate that (1) dynamic texture recognition is relatively insensitive to inter-patient variation while (2) viewing angle variability needs to be accounted for in the template database. Since naively extending the template database to counteract variability issues can lead to impractical database sizes, we propose an alternative strategy based on automated identification of a small set of representative models.

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