1 March 2006 Contextual-based Hopfield neural network for medical image edge detection
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Abstract
Detection and outlining of boundaries of organs and tumors in computed tomography (CT) and magnetic resonance imaging (MRI) images are prerequisite in medical applications. A special design Hopfield neural network called the contextual Hopfield neural network (CHNN) is presented for finding the edges of CT and MRI images. Different from the conventional 2-D Hopfield neural networks, the CHNN maps the 2-D Hopfield network at the original image plane. With the direct mapping, the network is capable of incorporating pixels' contextual information into an edge-detecting procedure. As a result, the effect of tiny details and noise will be effectively removed by the CHNN. Furthermore, the problem of satisfying strong constraints can be alleviated and results in a fast converge. Our experimental results show that the CHNN can obtain more appropriate, more continued edge points than Laplacian-based, Marr-Hildreth's, Canny's, wavelet-based, and CHEFNN methods.
© (2006) Society of Photo-Optical Instrumentation Engineers (SPIE)
Chuan-Yu Chang, "Contextual-based Hopfield neural network for medical image edge detection," Optical Engineering 45(3), 037006 (1 March 2006). https://doi.org/10.1117/1.2185488 . Submission:
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