In this paper, we present some contributions to improve a previous work's approach presented for the segmentation of magnetic resonance images of the human brain, based on the unsupervised Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of errors' squares, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be more close to the global minimum. Also, to ensure the convergence of the network and its utility in clinic with useful results, the minimization is achieved with a step function permitting the network to reach its stability corresponding to a local minimum close to the global minimum in a prespecified period of time. We present here our approach segmentations results of a patient data diagnosed with a metastatic tumor in the brain, and we compare them to those obtained based on, previous works using Hopfield neural networks, Boltzmann machine and the conventional ISODATA clustering technique.

Three-dimensional vocal tract shapes of a male, a female and a child subjects are measured from magnetic resonance (MR) images during sustained phonation of Japanese vowels /a, i, u, e, o/. Non-uniform dimensional differences in the vocal tract shapes of the subjects are quantitatively measured. Vocal tract area functions of the female and child subjects are normalized to those of the male on the basis of non-uniform and uniform scalings of the vocal tract length and compared with each other. A comparison is also made between the formant frequencies computed from the area functions normalized by the two different scalings. It is suggested by the comparisons that non-uniformity in the vocal tract dimensions is not essential in the normalization of the five Japanese vowels.

MRI is a widely used diagnostic imaging modality because it has excellent diagnostic capabilities, is safe to use and generates images not affected by bone artifacts. Images are obtained by utilizing the phenomenon of Nuclear Magnetic Resonance (NMR) by which protons located in a static magnetic field absorb radiofrequency (RF) pulses with a specific frequency and release a part of the energy as a NMR signal. Potentially MRI has the ability to provide functional and metabolic information (such as flow, temperature, diffusion, neuron activity) in addition to morphological information. This paper describes the imaging principles and provides a general outline of some applications: flow imaging, metabolite imaging and temperature imaging.