Modification of the mean-square error principle to double the convergence speed of a special case of Hopfield neural network used to segment pathological liver color images

• Main Authors: Sammouda Mohamed, Sammouda Rachid
• Format: Article
• Language: English
• Published: BMC 2004-12-01
• Series: BMC Medical Informatics and Decision Making
• Online Access: http://www.biomedcentral.com/1472-6947/4/22

<p>Abstract</p> <p>Background</p> <p>This paper analyzes the effect of the mean-square error principle on the optimization process using a Special Case of Hopfield Neural Network (SCHNN).</p> <p>Methods</p> <p>The segmentation of multidimensional medical and colour images can be formulated as an energy function composed of two terms: the sum of squared errors, and a noise term used to avoid the network to be stacked in early local minimum points of the energy landscape.</p> <p>Results</p> <p>Here, we show that the sum of weighted error, higher than simple squared error, leads the SCHNN classifier to reach faster a local minimum closer to the global minimum with the assurance of acceptable segmentation results.</p> <p>Conclusions</p> <p>The proposed segmentation method is used to segment 20 pathological liver colour images, and is shown to be efficient and very effective to be implemented for use in clinics.</p>