Neural image processing by dendritic networks

Diagnosis of brain tumor using image processing and determination of its type with RVM neural networks

Typically, the diagnosis of a tumor is done through surgical sampling, which is more precise with existing methods. The difference is that this is an aggressive, time consuming and expensive way. In the statistical method, due to the complexity of the brain tissues and the similarity between the cancerous cells and the natural tissues, even a radiologist or an expert physician may also be in er...

Neural Networks for Textured Image Processing

We review key conventional and neural network techniques for processing of textured images, and highlight the relationships among different methodologies and schemes. Texture, which provides useful information for segmentation of scenes, classification of surface materials and computation of shape, is exploited by sophisticated biological vision systems for image analysis. A brief overview of b...

Digital Image Processing with Neural Networks

Basic Cellular Neural Networks Image Processing

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic ...

Image processing with neural networks - a review

We review more than two hundred applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task...