Segmentation with Neural Networks

Wismueller, Axel

doi:10.1016/b978-012373904-9.50015-5

Cited by 2 publications

(1 citation statement)

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“…The neural module provides user-friendly tools to train and test the pattern vectors using multilayer perceptron neural network. [ 28 ] Figure 12 shows the training and testing panel of the MLP neural network implemented in Prometheus. The training panel receives the details about the pattern vectors using the text file which follows the standard format illustarted in Figure 11 (b) .…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Development of image-processing software for automatic segmentation of brain tumors in MR images

Chinnadurai

Gharpure

2011

J Med Phys

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Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ‘Prometheus,’ which performs neural system–based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively.

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Section: Multilayer Perceptronmentioning

confidence: 99%