Image segmentation plays a crucial role in many medical imaging applications and is an important but inherently difficult problem. This paper discusses the method that classifies unsupervised image using a Kohonen self-organizing map neural network. This method has two problems: training time of the network is too long and the classified result and quantity are much easily influenced by the noi...

Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied private or sensitive data such as patient medical records, the published may divulge critical information. In order address this issue, we propose differential method (SSKM_DP) based on SFLA-Kohonen network, which perturbs attributes maximum informatio...

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characterist...

The paper presents the application of the hybrid neural network to the solution of the calibration problem of the solid state sensor array used for the gas analysis. The applied neural network is composed of two parts: the selforganizing Kohonen layer and multilayer perceptron (MLP). The role of the Kohonen layer is to perform the feature extraction of the data and MLP network fulfills role of ...

This work is devoted to describe a potential use of the 1-Dimensional Kohonen Networks in the automatic non-supervised segmentation and classification of computed tomography brain slices. Possible perspectives of application include the automatic delineation of areas on the cerebral map and the automatic correlation between new clinical cases with previous boarded and closed cases. The classifi...

Dimension-reducing feature extraction neural network techniques which also preserve neighbourhood relationships in data have traditionally been the exclusive domain of Kohonen self organising maps. Recently, we introduced a novel dimension-reducing feature extraction process, which is also topographic, based upon a Radial Basis Function architecture. It has been observed that the gener-alisatio...

A new high capacity image steganography method based on kohonen neural network is introduced. Kohonen network is trained according to the absolute contrast sensitivity of pixels present in cover image. Trained network classify the pixels in different classes of sensitivity. Data embedding is performed in less sensitive pixels by LSB substitution method, which replaces the least significant bits...