Research Interests
مجالات البحث
Rough Sets and its applications
Statistics and Bayes Theory
Prediction and Bayesian Estimation for complete and censored Samples
Visualization, Image classification , Large Data Visualization, Visualization in Medicine and Life Sciences
Rough set theory is an approach and a methodology to handle ambiguity or uncertainty. We will write this methodology into a programmed system, where the user specifies the classes in the data by selecting particular districts in 2d slices.
Rough set theory used to compute lower and upper
approximation of the classes. The boundary region between the lower and the
upper approximations represents the ambiguity or doubt of the classification. We will present an
approach to automatically compute classification rules from the rough set classification
using k-means approach. The rule generation removes redundancies, which allows
us to enhance the original feature space attributes with a number of further
feature and object space attributes. The rules can be transferred from one 2D
slice to the entire 3D data set to produce a 3D segmentation result. The result
can be refined by the user interactively adding more samples (from the same or
other 2D slices) to the respective classes. Our system allows for visualization
of both the classification result and the uncertainty of the individual class
representations. The methods can be applied to single, as well as, multichannel imaging data. we will apply it to
medical imaging data with RGB (red green blue) color channels.
