Fatih Nar

An example based rendering (EBR) method based on generalization and localization that uses artificial neural networks (ANN) and k-Nearest Neighbor (k-NN) is proposed. The method involves learning phase and application phase, which means that once a transformation filter is learned, it can be applied to any other image. In learning phase, error back-propagation learning algorithm is used to learn general transformation filter using unfiltered source image and filtered output image. ANNs are usually unable to learn filter-generated textures and brush strokes hence these localized features are stored in a feature instance table for using with k-NN during application phase. In application phase, for any given grayscale image, first ANN is applied then k-NN search is used to retrieve local features from feature instances considering texture continuity to produce desired image. Proposed method is applied up to 40 image filters that are collection of computer-generated and human-generated effects/styles. Good results are obtained when image is composed of localized texture/style features that are only dependent to intensity values of pixel itself and its neighbors.

Automatic target detection methods for synthetic aperture radar (SAR) images are sensitive to image resolution, size of the target to be detected, clutter complexity, and speckle noise level. A robust automatic target detection method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based automatic target detection method which can find a target and its heterogeneous clutter independent of the image resolution and the target size has been developed. The proposed method provides efficient memory usage and low computational complexity.

Cortical renal (kidney) scintigraphy images are 2D images (256x256) acquired in three projection angles (posterior, right-posterior-oblique and left-posterior-oblique). These images are used by nuclear medicine specialists to examine the functional morphology of kidney parenchyma. The main visual features examined in reading the images are: size, location, shape and activity distribution (pixel intensity distribution within the boundary of each kidney). Among the above features, activity distribution (in finding scars if any) was found to have the least interobserver reproducibility. Therefore, in this study, we developed an image-based retrieval (IBR) and a computer-based diagnosis (CAD) system, focused on this feature in particular. The developed IBR and CAD algorithms start with automatic segmentation, boundary and landmark detection. Then, shape and activity distribution features are computed. Activity distribution feature is obtained using the acquired image and image set statistics of the normal patients. Active Shape Model (ASM) technique is used for more accurate kidney segmentation. In the training step of ASM, normal patient images are used. Retrieval performance is evaluated by calculating precision and recall. CAD performance is evaluated by specificity and sensitivity. To our knowledge, this paper is the first IBR or CAD system reported in the literature on renal cortical scintigraphy images.