Share Email Print

Proceedings Paper

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease
Author(s): Ingmar Voigt; Dime Vitanovski; Razvan I. Ionasec; Alexey Tsymal; Bogdan Georgescu; Shaohua K. Zhou; Martin Huber; Nassir Navab; Joachim Hornegger; Dorin Comaniciu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively.

Paper Details

Date Published: 12 March 2010
PDF: 10 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762314 (12 March 2010); doi: 10.1117/12.843972
Show Author Affiliations
Ingmar Voigt, Siemens Corp. Technology (Germany)
Univ. of Erlangen-Nuremberg (Germany)
Dime Vitanovski, Siemens Corp. Technology (Germany)
Razvan I. Ionasec, Siemens Corp. Research (United States)
Technical Univ. Munich (Germany)
Alexey Tsymal, Siemens Corp. Technology (Germany)
Bogdan Georgescu, Siemens Corp. Research (United States)
Shaohua K. Zhou, Siemens Corp. Research (United States)
Martin Huber, Siemens Corp. Technology (Germany)
Nassir Navab, Technische Univ. München (Germany)
Joachim Hornegger, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Dorin Comaniciu, Siemens Corp. Research (United States)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

© SPIE. Terms of Use
Back to Top