Computerized Tumor Boundary Detection Using a Hopfield Neural Network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)55-67
Journal / PublicationIEEE Transactions on Medical Imaging
Issue number1
Publication statusPublished - Feb 1997
Externally publishedYes


In this paper, we present a new approach for detection of brain tumor boundaries in medical images using a Hopfleld neural network. The boundary detection problem is formulated as an optimization process that seeks the boundary points to minimize an energy functional based on an active contour model. A modified Hopfield network is constructed to solve the optimization problem. Taking advantage of the collective computational ability and energy convergence capability of the Hopfield network, our method produces the results comparable to those of standard snakes-based algorithms, but it requires less computing time. With the parallel processing potential of the Hopfield network, the proposed boundary detection can be implemented for real time processing. Experiments on different magnetic resonance imaging (MRI) data sets show the effectiveness of our approach.

Research Area(s)

  • Active contour model, Boundary detection, Hopfield network, Magnetic resonance imaging