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.

The aim of this study was to quantify the effects of inhomogeneities on magnetocardiography (MCG) forward solutions. It can serve to guide the selection of inhomogeneities to include in any geometric model used to compute magnetocardiographics fields. A numerical model of a human torso was used which construction included geometry for major anatomical structures such as subcutaneous fat, skeletal muscle, lungs, major arteries and veins, and the bones. Simulations were done with a single current dipole placed at different sites of heart. The boundary element method (BEM) was utilized for numerical treatment of magnetic field calculations. Comparisons of the effects of different conductivity on MCG forward solution followed one of two basic schemes: 1) consider the difference between the magnetic fields of the homogeneous torso model and the same model with one inhomogeneity of a single organ or tissue added; 2) consider the difference between the magnetic fields of the full inhomogeneous model and the same model with one inhomogeneity of individual organ or tissue removed. When single inhomogeneities were added to an otherwise homogeneous model, the skeletal muscle, the right lung, the both lungs and the left lung had larger average effects (15.9, 15.1, 14.9, 14.4% relative error (RE), respectively) than the other inhomogeneities tested. When single inhomogeneities were removed from an otherwise full inhomogeneneous model, the both lungs, the left lung, and the skeletal muscle and the right lung had larger effects (17.3, 14.9, 14.3, 14.2% relative error (RE) respectively) than other inhomogeneities tested. The results of this study suggested that accurate representation of tissue inhomogeneity has a significant effect on the accuracy of the MCG forward solution. Our results showed that the inclusion of the boundaries also had effects on the topology of the magnetic fields and on the MCG inverse solution accuracy.

In our current work, we attempt to make an automatic diagnostic system of lung cancer based on the analysis of the sputum color images. In order to form general diagnostic rules, we have collected a database with thousands of sputum color images from normal and abnormal subjects. As a first step, in this paper, we present a segmentation method of sputum color images prepared by the Papanicalaou standard staining method. The segmentation is performed based on an energy function minimization using an unsupervised Hopfield neural network (HNN). This HNN have been used for the segmentation of magnetic resonance images (MRI). The results have been acceptable, however the method have some limitations due to the stuck of the network in an early local minimum because the energy landscape in general has more than one local minimum due to the nonconvex nature of the energy surface. To overcome this problem, we have suggested in our previous work some contributions. Similarly to the MRI images, the color images can be considered as multidimensional data as each pixel is represented by its three components in the RGB image planes. To the input of HNN we have applied the RGB components of several sputum images. However, the extreme variations in the gray-levels of the images and the relative contrast among nuclei due to unavoidable staining variations among individual cells, the cytoplasm folds and the debris cells, make the segmentation less accurate and impossible its automatization as the number of regions is difficult to be estimated in advance. On the other hand, the most important objective in processing cell clusters is the detection and accurate segmentation of the nuclei, because most quantitative procedures are based on measurements of nuclear features. For this reason, based on our collected database of sputum color images, we found an algorithm for NonSputum cell masking. Once these masked images are determined, they are given, with some of the RGB components of the raw image, to the input of HNN to make a crisp segmentation by assigning each pixel to label such as Background, Cytoplasm, and Nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully.