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PubMed2001

Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network.

In order to automate routine fecal examination for parasitic diseases, we propose in this study a computer processing algorithm using digital image processing techniques and an artificial neural network (ANN) classifier. The morphometric characteristics of eggs of human parasites in fecal specimens were extracted from microscopic images through digital image processing. An ANN then identified the parasite species based on those characteristics. We selected four morphometric features based on three morphological characteristics representing shape, shell smoothness, and size. A total of 82 microscopic images containing seven common human helminth eggs were used. The first stage (ANN-1) of the proposed ANN classification system isolated eggs from confusing artifacts. The second stage (ANN-2) classified eggs by species. The performance of ANN was evaluated by the tenfold cross-validation method to obviate the dependency on the selection of training samples. Cross-validation results showed 86.1% average correct classification ratio for ANN-1 and 90.3% for ANN-2 with small variances of 46.0 and 39.0, respectively. The algorithm developed will be an essential part of a completely automated fecal examination system.

Algorithms↗

PubMed2002

Neural network surface acoustic wave RF signal processor for digital modulation recognition.

An architecture of a surface acoustic wave (SAW) processor based on an artificial neural network is proposed for an automatic recognition of different types of digital passband modulation. Three feed-forward networks are trained to recognize filtered and unfiltered binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals, as well as unfiltered BPSK, QPSK, and 16 quadrature amplitude (16QAM) signals. Performance of the processor in the presence of additive white Gaussian noise (AWGN) is simulated. The influence of second-order effects in SAW devices, phase, and amplitude errors on the performance of the processor also is studied.

Journal Article↗

PubMedDate not supplied

Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.

Artificial neural networks have been applied to the differentiation of actual "true" clusters from normal parenchymal patterns and also to the differentiation of actual clusters from false-positive clusters as reported by a computerized scheme for the detection of microcalcifications in digital mammograms. The differentiation was carried out in both the spatial and frequency domains. The performance of the neural networks was evaluated quantitatively by means of receiver operating characteristic (ROC) analysis. It was found that the networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false-positive clusters of microcalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme. A large, comprehensive training database is needed for neural networks to perform reliably in clinical situations.

Breast Diseases↗

PubMed1996

Effects of horizontal cell network architecture on signal spread in the turtle outer retina. Experiments and simulations.

In the Pseudemys turtle retina five functionally distinct, electrically coupled networks of horizontal cells distribute signals in the outer plexiform layer. These networks differ significantly in their architecture, as determined by intracellular labeling with Neurobiotin after physiological recording and identification. The density of H1 horizontal cells is highest, ranging around 1800 cells/mm2 at approximately 2.3 mm eccentricity. H1 horizontal cell somata are connected via 6-10 thin, short dendrites. The H1 horizontal cell axon terminal network is composed of thick axon terminals, forming a three-dimensional, sheath-like structure. Networks of coupled H2 and H3 horizontal cells have cell densities of around 210 cells/mm2 and 350 cells/mm2, respectively, at the same eccentricity of 2.3 mm. Cell bodies are connected with 6-12 long, thin dendrites. Here we report for the first time H4 horizontal cell networks. Cell density is approximately 970 cells/mm2 at 2 mm eccentricity, and cell bodies are connected with 6-10 thin, short dendrites. General properties of passive voltage spread were compared for three of these horizontal cell networks using NeuronC. Realistic network architectures were obtained by digitizing the intracellularly labeled networks, respectively. One network obtained from coupled H1 horizontal cell bodies, one from coupled H1 horizontal cell axon terminals, and one from H2 horizontal cells were simulated. These three realistic networks were compared with an artificial, electrically coupled regular triangular network. Passive signal spread in these networks strongly depended on the exact network architecture using otherwise identical parameters. Changes in coupling strength affected signal spread in these networks differently. As in the experimental situation, changes in synaptic conductance influenced signal spread. Some principal effects of extensively coupled horizontal cells on photoreceptor signal processing were simulated with one type of photoreceptor connected by telodendria, synapsing onto an underlying triangular network and receiving feedback synapses. Under certain conditions, spatial information is coded in single photoreceptors. This was also the case in the experimental situation. In the simulation, spatial filter adjustment for optimal spatial coding in photoreceptors can be achieved by changing coupling strength in the horizontal cell network.

Animals↗

PubMed1991

Digital imaging in the radiation oncology environment: a personal computer local area network solution.

Personal computer (PC) driven digital imaging devices are now becoming available in the market place for use in radiation oncology to produce what are known as real time portal images. These limited contrast images are used to verify the patient anatomy under treatment by megavoltage x-rays and are a vital part of the patient treatment. The current generation of devices can produce both single and movie loop images in real time for physician review and approval. To disseminate these images and other digital images used in the planning and delivery of radiation therapy, a PC-based picture archiving and communications system has been developed that is tailored to the special needs of radiation oncology.

Humans↗

PubMed2004

Neural network approach for modification and fitting of digitized data in reverse engineering.

Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.

Algorithms↗

PubMed1983

A distribution system for digital images from diverse image sources. Incorporating a local area network in an imaging environment.

This paper proposes a unified image-processing and viewing system as a viewing station and initially as the central file server in a unified digital image distribution and processing network, linking various digital image sources through a high speed data link and a common image format. The network allows for viewing and processing of all images produced within the complex and for locating viewing stations in any number of convenient areas. The system proposed can be slowly expanded to include all the digital images produced within the department of institution.

Computers↗

PubMed2002

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study.

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

Humans↗

PubMed2005

Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network.

We evaluated the CellaVision DM96 (CellaVision AB, Lund, Sweden), an automated digital cell morphology and informatics system for peripheral blood smears. Technologists agreed with 82% of the instrument's preclassifications. Correlation coefficients between final results released from the CellaVision and results obtained by direct microscopy were 0.96 (all neutrophils), 0.94 (lymphocytes), 0.88 (segmented neutrophils), 0.73 (eosinophils), 0.69 (bands), and 0.67 (monocytes). After correction for statistically and clinically insignificant variations, the CellaVision DM96 had 95% sensitivity and 88% specificity for immature myeloid cells. It was 100% sensitive and 94% specific for blasts, and 100% sensitive and 97% specific for unusual WBCs and nucleated RBCs. Advantages of the CellaVision DM96 over direct microscopy include the ability to review slides from a remote location, consultation and quality control on a cell-by-cell basis, and potential labor savings.

Cost-Benefit Analysis↗

PubMed2001

[Reconstruction of digital avulsion with pre-fabricated subdermal vascular network skin flap by ultrasonic liposuction].

OBJECTIVE: To investigate the clinical application of subdermal vascular network skin flap pre-fabricated by ultrasonic liposuction in reconstruction of digital avulsion. METHODS: Forty-seven injured fingers of 23 cases were treated from June 1997 to February 2000. Conventional abdominal skin flap was elevated, according to the size of digital avulsion, and subcutaneous fat was removed with scissors. Ultrasonic liposuction technology was adopted, in order to minimize the injury of subdermal vascular vessels, to remove the fat particles close to the vascular network. Finally, the pre-fabricated skin flap was used to repair the digital avulsion. The vascular pedicle was severed in 5 to 7 days after operation. The range of skin flap was 4 cm x 3 cm to 8 cm x 7 cm, and the ratio of length and width was (2 to 3) to 1. RESULTS: All the skin flaps were survived. Twenty-one patients were available for postoperative follow-up for 6 to 24 months. The motion of interphalangeal joint achieved functional recovery, and the sensation of pain, temperature and taction recovered well. CONCLUSION: Ultrasonic liposuction does not obviously injure the subdermal vascular network skin flap, it is a simple and safe method for treatment of digital avulsion.

Adolescent↗

PubMed1994

[Clinical experiences with PACS: digital radiology].

We present our experience during the first 21 months of using hospital-wide network technology and digital archiving in connection with digital radiology in the Radiology Department at the SMZO/Danube hospital in Vienna. This means digital generation, archiving and distribution of radiographs as well as monitor reporting embedded in HIS and RIS. The clinical use of PACS demands full integration of all subsystems and modalities in a digital way, as was first realized at the Danube Hospital. With this approach, a reduction in radiation dose, improved communication and thus a reduction in the length of hospital stay and health care cost are attained.

Austria↗

PubMed1997

A preliminary laboratory investigation of air embolus detection and grading using an artificial neural network.

SUMMARY STATEMENT: Processed digitized Doppler signals abstracted from recordings during continuous air infusion in dogs were used to train a neural network to estimate air embolism infusion rates. BACKGROUND: Precordial Doppler is a sensitive technique for detecting venous air embolism during anesthesia, but it requires constant attentive listening. Since neural networks are particularly well suited to the task of pattern recognition, we sought to investigate this technology for detection and grading of air embolism. METHODS: Air was infused into peripheral veins of four anesthetized dogs at rates of 0.025, 0.05, 0.10, 0.25, 0.50 and 1.0 ml-1.kg-1.min-1 while digital recordings of the precordial Doppler ultrasound signal were collected. The frequency content of the recordings was determined by Fourier analysis. The output of the Fourier transform was the input to a neural network. The network was then trained to estimate the air infusion rate. RESULTS: The correlation coefficient between the size of the air embolism and the air infusion rate was greater than r2 = 0.93 for each of the four animals in the study when the network was trained using the data for all four dogs. When the data from a dog was withheld from the training set and used only for testing the correlation coefficients ranged from r2 = 0.75 to r2 = 0.27. For frequencies below 250 Hz, the acoustic energy tended to fall as the air infusion rate increased. The opposite occurred at frequencies above 325 Hz. CONCLUSIONS: Neural network processing of the precordial Doppler signal provides a quantitative estimate of the size of an air embolism.

Anesthesia↗

PubMed1998

[Electromagnetic interference between automatic defibrillators and digital and analog cellular telephones].

BACKGROUND AND OBJECTIVES: Functional pacemaker interference by mobile telephones has been described with analogical systems and with possible greater influence, digital systems, including inhibition and inadequate pacing. The influence of both system has not been extensively studied in patients with implantable cardioverter defibrillators (ICD). PATIENTS AND METHODS: We studied the influence of mobile phones, both digital and analogic network, on the performance of several models of defibrillators, in a standardised test set up designed to provide high sensitivity. The purpose of our study was to establish whether there are any influences on ICD functions, both in in vivo and in in vitro models. Several mobile phones, with different transmission powers, were moved towards the defibrillator and the electrode, under continuous documentation of defibrillator sensing and interrogation afterwards. The experimental model was performed with the aid of an arrhythmia simulator (Intersim) and demo-defibrillators. The tests were repeated both in and out of a solution of saline water with an impedance within normal human limits. RESULTS: Partial loss of telemetry was found in 14 patients, 8 with analogical phones and 6 with digital phones. Fourteen patients showed alterations only on the surface electrocardiogram channel and five on the intracavitary channel. The same results were reproduced in the in vitro model. However, the in vitro test allowed us to simulate multiple ventricular arrhythmias, and demonstrate the normal sensing and functioning of the defibrillator during a "spontaneous" arrhythmia. After testing, we demonstrate that no real oversensing/undersensing was documented in any device. There was no evidence of ICD reprogramming or pacing inhibition. In particular, no inadequate therapies were delivered. CONCLUSIONS: a) in our series, we have not demonstrated clinically significant electromagnetic interferences with mobile phones of digital or analogical networks: b) the in vitro model allowed us to conclude that even if a spontaneous arrhythmia appears, the function of the defibrillator is not altered; c) the use of mobile phones seems to be safe for defibrillator patients, and d) however, some basic rules, such as to maintain the phone at least 15 cm away from the defibrillator, are advised.

Defibrillators, Implantable↗

PubMed1995

Comparison of multilayer neural network and Nearest Neighbor Classifiers for handwritten digit recognition.

The basic Nearest Neighbor Classifier (NNC) is often inefficient for classification in terms of memory space and computing time needed if all training samples are used as prototypes. These problems can be solved by reducing the number of prototypes using clustering algorithms and optimizing the prototypes using a special neural network model. In this paper, we compare the performance of the multilayer neural network and an Optimized Nearest Neighbor Classifier (ONNC) for handwritten digit recognition applications. We show that an ONNC can have the same recognition performance as an equivalent neural network classifier. The ONNC can be efficiently implemented using prototype and variable ranking, partial summation and distance triangular inequality based strategies. It requires the same memory space as, but less, training time and classification time than the neural network.

Computers↗

PubMed1992

Classification of crossed immunoelectrophoretic patterns using digital image processing and artificial neural networks.

A method is presented which makes it possible to present crossed immunoelectrophoretic patterns to an artificial neural network. The electrophoretic patterns are presented for the artificial neural network as three-dimensional vectors and it is shown that it is possible with this representation to train the network to learn the patterns and classify them. It was found that the ability to generalize was substantially increased by the addition of noise to the input patterns during training. Furthermore, the addition of noise decreased the number of presentations needed to reach the predetermined error level. The trained neural network was able to classify all distorted patterns correctly within an error range of 1%.

Image Processing, Computer-Assisted↗

PubMed1998

Report of a national neurosurgical emergency teleconsulting system.

OBJECTIVE: The goal was to develop a low-cost, national, neurosurgical emergency teleconsulting system that is independent of vendor computed tomographic (CT) or magnetic resonance imaging (MRI) scanner type. METHODS: Charge-coupled device scanners are used to digitize hard copies of CT and MRI scans. An enhanced optical density range is achieved by using an algorithm to fuse data from multiple exposures at different integration periods. The system is based on personal computers using Microsoft Windows 3.11. Data are transmitted on a wide-area network at 128 kilobits/s, over Integrated Systems Digital Network lines. The network connects both neurosurgical departments in Ireland to all major hospitals with CT/MRI scanners. RESULTS: The scanner optical density is 0.05 to 3.0, with 2.24 to 2.5 line pairs/mm. Five-megabyte images are transmitted uncompressed in 6 minutes. To date, more than 750 CT and MRI scans have been transmitted. The system is completely automated, and operator acceptance has been very high. Images are automatically stored and displayed at the receiving workstation, where the images can be viewed and manipulated on-screen. This system has significantly enhanced acute neurosurgical patient care. CONCLUSION: The system is cost effective and simple to use, has gained widespread physician acceptance, and delivers an image quality superior to that of many commercially available systems.

Angiography↗

PubMed1996

Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance.

The performance of a new, neural network-based image compression method was evaluated on digital radiographs for use in an educational environment. The network uses a mixture of principal components (MPC) representation to effect optimally adaptive transform coding of an image and has significant computational advantages over other techniques. Nine representative digital chest radiographs were compressed 10:1, 20:1, 30:1, and 40:1 with the MPC method. The five versions of each image, including the original, were shown simultaneously, in random order, to each of seven radiologists, who rated each one on a five-point scale for image quality and visibility of pathologic conditions. One radiologist also ranked four versions of each of the nine images in terms of the severity of distortion: The four versions represented 30:1 and 40:1 compression with the MPC method and with the classic Karhunen-Loève transform (KLT). Only for the images compressed 40:1 with the MPC method were there any unacceptable ratings. Nevertheless, the images compressed 40:1 received a top score in 26%-33% of the evaluations. Images compressed with the MPC method were rated better than or as good as images compressed with the KLT technique 17 of 18 times. Four of nine times, images compressed 40:1 with the MPC method were rated as good as or better than images compressed 30:1 with the KLT technique.

Neural Networks, Computer↗

PubMed2006

Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity.

We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits. At the single-cell level, we use custom-designed analog circuits (ASICs) that simulate two types of spiking neurons based on Hodgkin-Huxley like dynamics: "regular spiking" excitatory neurons with spike-frequency adaptation, and "fast spiking" inhibitory neurons. Synaptic interactions are mediated by conductance-based synaptic currents described by kinetic models. Connectivity and plasticity rules are implemented digitally through a real time interface between a computer and a PCI board containing the ASICs. We show a prototype system of a few neurons interconnected with synapses undergoing spike-timing dependent plasticity (STDP), and compare this system with numerical simulations. We use this system to evaluate the effect of parameter dispersion on the behavior of small circuits of neurons. It is shown that, although the exact spike timings are not precisely emulated by the ASIC neurons, the behavior of small networks with STDP matches that of numerical simulations. Thus, this mixed analog-digital architecture provides a valuable tool for real-time simulations of networks of neurons with STDP. They should be useful for any real-time application, such as hybrid systems interfacing network models with biological neurons.

Action Potentials↗