Segmentation (Image Processing) - Important Concepts in Signal Processing, Image Processing and Data Compression - page 36

More recently, these ideas for multi-scale image segmentation by linking image
structures over scales have been picked up by Florack and Kuijper . Bijaoui and Rué
associate structures detected in scale-space above a minimum noise threshold into an
object tree which spans multiple scales and corresponds to a kind of feature in the
original signal. Extracted features are accurately reconstructed using an iterative
conjugate gradient matrix method.
Semi-automatic segmentation
In this kind of segmentation, the user outlines the region of interest with the mouse clicks
and algorithms are applied so that the path that best fits the edge of the image is shown.
Techniques like Siox, Livewire, Intelligent Scissors or IT-SNAPS are used in this kind of
segmentation.
Neural networks segmentation
Neural Network segmentation relies on processing small areas of an image using an
artificial neural network or a set of neural networks. After such processing the decision-
making mechanism marks the areas of an image accordingly to the category recognized
by the neural network. A type of network designed especially for this is the Kohonen
map.
Pulse-Coupled Neural Networks (PCNNs) are neural models proposed by modeling a
cat's visual cortex and developed for high-performance biomimetic image processing. In
1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual
cortex. The Eckhorn model provided a simple and effective tool for studying small
mammal's visual cortex, and was soon recognized as having significant application
potential in image processing. In 1994, the Eckhorn model was adapted to be an image
processing algorithm by Johnson, who termed this algorithm Pulse-Coupled Neural
Network. Over the past decade, PCNNs have been utilized for a variety of image
processing applications, including: image segmentation, feature generation, face
extraction, motion detection, region growing, noise reduction, and so on. A PCNN is a
two-dimensional neural network. Each neuron in the network corresponds to one pixel in
an input image, receiving its corresponding pixel's color information (e.g. intensity) as an
external stimulus. Each neuron also connects with its neighboring neurons, receiving
local stimuli from them. The external and local stimuli are combined in an internal
activation system, which accumulates the stimuli until it exceeds a dynamic threshold,
resulting in a pulse output. Through iterative computation, PCNN neurons produce
temporal series of pulse outputs. The temporal series of pulse outputs contain information
of input images and can be utilized for various image processing applications, such as
image segmentation and feature generation. Compared with conventional image
processing means, PCNNs have several significant merits, including robustness against
noise, independence of geometric variations in input patterns, capability of bridging
minor intensity variations in input patterns, etc.