by Younis, Akmal; Ibrahim, Mohamed; Kabuka, Mansur; John, Nigel

In this paper, a new neural network model inspired by the biological immune system functions is presented. The model, termed Artificial Immune-Activated Neural Network (AIANN), extracts classification knowledge from a training data set, which is then used to classify input patterns or vectors. The AIANN is based on a neuron activation function whose behavior is conceptually modeled after the chemical bonds between the receptors and epitopes in the biological immune system. The bonding is controlled through an energy measure to ensure accurate recognition. The AIANN model was applied to the segmentation of 3-dimensional magnetic resonance imaging (MRI) data of the brain and a contextual basis was developed for the segmentation problem. Evaluation of the segmentation results was performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.

DOI: 10.1007/s10278-007-9081-0
Online Date: 12/11/2007
Print publication date: 10/1/2008
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by Johnson, Pamela T.; Hahn, Horst K.; Heath, David G.; Fishman, Elliot K.

The preceding manuscript describes the principles behind the Interactive Watershed Transform (IWT) segmentation tool. The purpose of this manuscript is to illustrate the clinical utility of this editing technique for body multidetector row computed tomography (MDCT) imaging. A series of cases demonstrates clinical applications where automated segmentation of skeletal structures with IWT is most useful. Both CT angiography and orthopedic applications are presented.

DOI: 10.1007/s10278-007-9087-7
Online Date: 12/8/2007
Print publication date: 12/1/2008
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by Heath, David G.; Hahn, Horst K.; Johnson, Pamela T.; Fishman, Elliot K.

Segmentation of volumetric computed tomography (CT) datasets facilitates evaluation of 3D CT angiography renderings, particularly with maximum intensity projection displays. This manuscript describes a novel automated bone editing program that uses an interactive watershed transform (IWT) technique to rapidly extract the skeletal structures from the volume. Advantages of this tool include efficient segmentation of large datasets with minimal need for correction. In the first of this two-part series, the principles of the IWT technique are reviewed, followed by a discussion of clinical utility based on our experience.

DOI: 10.1007/s10278-007-9085-9
Online Date: 12/4/2007
Print publication date: 12/1/2008
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