An iterative classification method of 2D CT head data based on statistical and spatial information
Feng Li, Dirk Bartz, Lixu Gu, Michel Albert Audette · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
An iterative classification method developed for 2D CT head data classification problem and using both statistical and spatial information is introduced in this paper. The method reduces the chance of misclassification, preserving the contiguity of tissue classes. This method is minimally supervised so that it enforces a relation between tissues and classes. In later iterations high-confidence points are used to help classify nearby ambiguous points, based on the assumption that points in close proximity and of comparable intensities are probably representing the same tissue class.