title: Spatiotemporal Hopfield neural cube for diagnosing recurrent nasal papilloma.
abstract: Gadolinium-enhanced magnetic resonance imaging (MRI) is widely used to detect recurrent nasal tumours. A specifically designed two-layer Hopfield neural network, called the spatiotemporal Hopfield neural cube (SHNC), is presented, to be used for detecting recurrent nasal papilloma. Differing from conventional, two-dimensional Hopfield neural networks, the SHNC extends the one-layer, two-dimensional Hopfield network in the original image plane into a two-layer, three-dimensional (3D) Hopfield network with pixel classification implemented in its third dimension. With extended 3D architecture, the network is able to use each pixel's spatial information in a pixel labelling procedure. Because the SHNC takes pixel spatial information into consideration, the effects of tiny detail or noise are removed. As a result, the drawback of disconnected fractions can be avoided. Furthermore, owing to the incorporation of competitive learning rules to update neuron states, to avoid the problem of having to satisfy strong constraints, the convergence of the network was improved. In addition, a more accurate signal-time curve, the relative intensity change (RIC), was adopted to represent the gadolinium-enhanced MRI temporal information, and the RIC curves of recurrent nasal papilloma were incorporated into the SHNC. The experimental results showed that the SHNC could obtain a more appropriate, precise position of recurrent nasal papilloma than the k-means, principal components analysis (PCA) or Eigenimage-filtering methods. The average sensitivity and specificity of the 26 cases were 0.9998 and 0.9961, respectively. These values demonstrate the effectiveness of the proposed technique.
authors.affiliation: Department of Electronic Engineering, National Yunlin University of Science & Technology, Yunlin, Taiwan. chuanyu@yuntech.edu.tw
authors.forename: C Y
authors.initials: CY
authors.lastname: Chang
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis*; D016571:Neural Networks, Computer; D009669:Nose Neoplasms / Q000175:diagnosis*; D010212:Papilloma / Q000175:diagnosis*
references.citation: IEEE Trans Image Process. 1998;7(12):1730-5
references.pmid: 18276241
references.citation: AJNR Am J Neuroradiol. 1999 Sep;20(8):1445-51
references.pmid: 10512227
references.citation: IEEE Eng Med Biol Mag. 1999 Jul-Aug;18(4):100-5
references.pmid: 10429908
references.citation: J Comput Assist Tomogr. 1988 Jan-Feb;12(1):1-9
references.pmid: 3335646
references.citation: J Comput Assist Tomogr. 1991 Jul-Aug;15(4):621-8
references.pmid: 2061479
references.citation: IEEE Trans Med Imaging. 1996;15(4):560-7
references.pmid: 18215937
references.citation: Laryngoscope. 1995 Mar;105(3 Pt 1):282-8
references.pmid: 7877417
references.citation: Radiology. 1993 Dec;189(3):881-9
references.pmid: 8234720
references.citation: IEEE Trans Med Imaging. 1998 Dec;17 (6):872-80
references.pmid: 10048844