Perfect image segmentation using pulse coupled neural networks

Abstract

This paper describes a method for segmenting digital images using pulse coupled neural networks (PCNN's). The pulse coupled neuron (PCN) model used in PCNN is a modification of Eckhorn's cortical neuron model. A single layered laterally connected PCNN is capable of perfectly segmenting digital images even when there is a considerable overlap in the intensity ranges of adjacent regions. Conditions for perfect image segmentation are derived. It is also shown that addition of an inhibition receptive field to the neuron model increases the possibility of perfect segmentation. The inhibition input reduces the overlap of intensity ranges of adjacent regions by effectively compressing the intensity range of each region.

An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation

“…In this group of experiments, we also give the segmentation results of several other classical models, in which the level set-based segmentation methods have a CV model [12] and geodesic active contours (GAC) model [1], and the traditional segmentation methods include expectation maximization (EM) algorithm [40], Nobuyuki Otsu's (OTSU) algorithm [41], and pulse-coupled neural network (PCNN) algorithm [42]. In order to facilitate the subsequent description, we give each input image a number; the digital numbers corresponding to the input images of Figure 3 are 1 to 6.…”

Automating the processing of cDNA microarray images

“…The problem faced in this algorithm is the method of automatic seed selection. Alternatively, pulsed coupled neural networks (PCNN) (Kuntimad and Ranganath, 1999) have been utilised in image segmentation. The general approach to segment images using PCNN is to adjust the parameters of the network so that the neurons corresponding to the pixels of a given region pulse together and the neurons corresponding to the pixels of adjacent regions do not pulse together.…”

An Advanced Neural Network-Based Approach for Military Ground Vehicle Recognition in Sar Aerial Imagery

“…The pulse is able to feed back to modulate the threshold E ij [n] via a leaky integrator, raising the threshold by magnitude that decreases with time constant α E. During iterations, when a neuron's internal activity exceeds its dynamic threshold E ij [n], a pulse is generated (firings). The PCNN model has proved to be very suitable for image segmentation [6], [9], [10], and [15]. To obtain an improved segmentation performance, we have proposed to run in parallel two PCNN segmentation models, one for n iterations and second for m iterations (see Fig.…”

“…We further propose application and development of new and challenging neural network models both for target detection (filtering and segmentation) and also for classification tasks. The segmentation uses the pulse-coupled neural network (PCNN) model based on the implementation of the mechanisms underlying the visual cortex of cat [9], [10], [14], [15]. The visual cortex is the part of the brain that receives information from the eye.…”