PURPOSE: This paper proposes the discriminative generalized Hough transform (DGHT) as an efficient and reliable means for object localization in medical images. It is meant to give a deeper insight into the underlying theory and a comprehensive overview of the methodology and the scope of applications. METHODS: The DGHT combines the generalized Hough transform (GHT) with a discriminative training technique for the GHT models to obtain more efficient and robust localization results. To this end, the model points are equipped with individual weights, which are trained discriminatively with respect to a minimal localization error. Through this weighting, the models become more robust since the training focuses on common features of the target object over a set of training images. Unlike other weighting strategies, our training algorithm focuses on the error rate and allows for negative weights, which can be employed to encode rivaling structures into the model. The basic algorithm is presented here in conjunction with several extensions for fully automatic and faster processing. These include: (1) the automatic generation of models from training images and their iterative refinement, (2) the training of joint models for similar objects, and (3) a multi-level approach. RESULTS: The algorithm is tested successfully for the knee in long-leg radiographs (97.6 % success rate), the vertebrae in C-arm CT (95.5 % success rate), and the femoral head in whole-body MR (100 % success rate). In addition, it is compared to Hough forests (Gall et al. in IEEE Trans Pattern Anal Mach Intell 33(11):2188-2202, 2011) for the task of knee localization (97.8 % success rate). Conclusion The DGHT has proven to be a general procedure, which can be easily applied to various tasks with high success rates.
PURPOSE: This paper proposes the discriminative generalized Hough transform (DGHT) as an efficient and reliable means for object localization in medical images. It is meant to give a deeper insight into the underlying theory and a comprehensive overview of the methodology and the scope of applications. METHODS: The DGHT combines the generalized Hough transform (GHT) with a discriminative training technique for the GHT models to obtain more efficient and robust localization results. To this end, the model points are equipped with individual weights, which are trained discriminatively with respect to a minimal localization error. Through this weighting, the models become more robust since the training focuses on common features of the target object over a set of training images. Unlike other weighting strategies, our training algorithm focuses on the error rate and allows for negative weights, which can be employed to encode rivaling structures into the model. The basic algorithm is presented here in conjunction with several extensions for fully automatic and faster processing. These include: (1) the automatic generation of models from training images and their iterative refinement, (2) the training of joint models for similar objects, and (3) a multi-level approach. RESULTS: The algorithm is tested successfully for the knee in long-leg radiographs (97.6 % success rate), the vertebrae in C-arm CT (95.5 % success rate), and the femoral head in whole-body MR (100 % success rate). In addition, it is compared to Hough forests (Gall et al. in IEEE Trans Pattern Anal Mach Intell 33(11):2188-2202, 2011) for the task of knee localization (97.8 % success rate). Conclusion The DGHT has proven to be a general procedure, which can be easily applied to various tasks with high success rates.
Authors: M Grass; R Koppe; E Klotz; R Proksa; M H Kuhn; H Aerts; J Op de Beek; R Kemkers Journal: Comput Med Imaging Graph Date: 1999 Nov-Dec Impact factor: 4.790
Authors: Olivier Pauly; Ben Glocker; Antonio Criminisi; Diana Mateus; Axel Martinez Möller; Stephan Nekolla; Nassir Navab Journal: Med Image Comput Comput Assist Interv Date: 2011
Authors: Olivier Ecabert; Jochen Peters; Hauke Schramm; Cristian Lorenz; Jens von Berg; Matthew J Walker; Mani Vembar; Mark E Olszewski; Krishna Subramanyan; Guy Lavi; Jürgen Weese Journal: IEEE Trans Med Imaging Date: 2008-09 Impact factor: 10.048
Authors: Tobias Heimann; Bram van Ginneken; Martin A Styner; Yulia Arzhaeva; Volker Aurich; Christian Bauer; Andreas Beck; Christoph Becker; Reinhard Beichel; György Bekes; Fernando Bello; Gerd Binnig; Horst Bischof; Alexander Bornik; Peter M M Cashman; Ying Chi; Andrés Cordova; Benoit M Dawant; Márta Fidrich; Jacob D Furst; Daisuke Furukawa; Lars Grenacher; Joachim Hornegger; Dagmar Kainmüller; Richard I Kitney; Hidefumi Kobatake; Hans Lamecker; Thomas Lange; Jeongjin Lee; Brian Lennon; Rui Li; Senhu Li; Hans-Peter Meinzer; Gábor Nemeth; Daniela S Raicu; Anne-Mareike Rau; Eva M van Rikxoort; Mikaël Rousson; László Rusko; Kinda A Saddi; Günter Schmidt; Dieter Seghers; Akinobu Shimizu; Pieter Slagmolen; Erich Sorantin; Grzegorz Soza; Ruchaneewan Susomboon; Jonathan M Waite; Andreas Wimmer; Ivo Wolf Journal: IEEE Trans Med Imaging Date: 2009-02-10 Impact factor: 10.048