Multidimensional pulse image processing of chemical structure data

• Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17 beta-estradiol.

Keyword

pulse-coupled neural networks

multidimensional pulse imaging preprocessing

surface detection

feature detection

3D-QSAR

3D-QSPR

neural-network

fusion

times

Publication and Content Type

ref (subject category)

art (subject category)