@InProceedings{Svec-IbPRIA2005,
  IS = { zkontrolovano 30 Nov 2005 },
  UPDATE  = { 2005-06-21 },
  author =	 {{\v S}vec, Martin and {\v S}{\' a}ra, Radim and
                  Smutek, Daniel},
  title =	 {On Reproducibility of Ultrasound Image
                  Classification},
  year =	 {2005},
  pages =	 {439-446},
  booktitle =	 {IbPRIA 2005: Proceedings of the Second Iberian
                  Conference on Pattern Recognition and Image
                  Analysis},
  editor =	 {Marques, Jorge S. and P{\' e}rez de la Blanca,
                  Nicol{\' a}s and Pina, Pedro},
  publisher =	 {Springer-Verlag},
  address =	 {Berlin, Germany},
  issn =	 {0302-9743},
  volume =	 {2},
  number =	 {2},
  book_pages =	 {733},
  month =	 {June},
  day =		 {7-9},
  venue =	 {Estoril, Portugal},
  organization = {International Association for Pattern Recognition},
  annote =	 {Ultrasound B-mode images of thyroid gland were
                  previously analyzed to distinguish normal tissue
                  from inflamed tissue due to Hashimoto's Lymphocytic
                  Thyroiditis. This is a two-class recognition
                  problem. Sensitivity and specificity of 100% was
                  reported using Bayesian classifier with selected
                  texture features. These results were obtained on 99
                  subjects at a fixed setting of one specific
                  sonograph, for a given manual thyroid gland
                  segmentation and sonographic scan orientation
                  (longitudinal, transversal). To evaluate the
                  reproducibility of the method, sensitivity analysis
                  is the topic of this paper. A general method for
                  determining feature sensitivity to variables
                  influencing the scanning process is proposed. Jensen
                  Shannon distances between modified and unmodified
                  inter- and intra-class feature probability
                  distributions capture the changes induced by the
                  variables. Among selected features, the least
                  sensitive one is found. The proposed sensitivity
                  evaluation method can be used in other problems with
                  complex and non-linear dependencies on variables
                  that cannot be controlled.},
  keywords =	 {sensitivity analysis, reproducibility, texture
                  classification},
  project =	 {1ET101050403, NO/7742-3, CTU 0506113},
  psurl =	 {[Svec-IbPRIA2005.pdf]
                  },
}