Neural attractor network for application in visual field data classification.

Wolfgang Fink

doi:10.1088/0031-9155/49/13/003

Home/Archive/PubMed

PubMed · 15285248

Neural attractor network for application in visual field data classification.

Wolfgang Fink

View original source ↗Export metadata ↓

Abstract

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Explore related subjects

Diagnosis, Computer-Assisted Humans Microcomputers Models, Theoretical Neural Networks, Computer Software Time Factors Vision Disorders Visual Field Tests Visual Fields Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Wolfgang Fink. 2004-07-07. Neural attractor network for application in visual field data classification.. https://doi.org/10.1088/0031-9155%2F49%2F13%2F003

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2005

Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance.

In this study, the digital transformation (digital staining) of the 16-band multispectral image of a hematoxylin and eosin (HE) stained pathological specimen to its Masson's trichrome (MT) stained counterpart is addressed. The digital staining procedure involves the classification of the various H&E-stained tissue components and then the transformation of their transmittance spectra to their equivalent MT-stained transmittance configurations. Combination of transmittance classifiers were designed to classify the various tissue components found in the multispectral images of an HE-stained specimen, e.g. nucleus, cytoplasm, red blood cell (RBC), fibrosis, etc.; while pseudo-inverse method was used to obtain the transformation matrices that would translate the transmittance spectra of the classified HE-stained multispectral pixels to their MT-stained configurations. To generate the digitally stained image, weighting factors, which were based on the classifiers beliefs, were introduced to the generated transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) to implement a digital staining framework in the pathological context.

Diagnosis, Computer-Assisted↗

PubMed2005

Diagnosing pneumonia in rural Thailand: Digital cameras versus film digitizers for chest radiograph teleradiology.

BACKGROUND: Accurate surveillance for pneumonia requires standardized classification of chest radiographs. Digital imaging permits rapid electronic transfer of data to radiologists, and recent improvements in digital camera technology present high quality, yet cheaper, options. METHODS: We evaluated the comparative utility of digital camera versus film digitizer in capturing chest radiographs in a pneumonia surveillance system in rural Thailand using a panel of radiologists; the gold standard was the hard-copy radiograph. We calculated sensitivity and specificity and conducted a receiver operator characteristics (ROC) analysis. RESULTS: Of the 192 radiographs from patients with clinical pneumonia, 166 (86%) were classified as pneumonia on the hard copies. Sensitivity and specificity for identifying pneumonia were 89% and 73% for the camera and 90% and 65% for the digitizer. In the ROC analysis, there was no statistically significant difference in the area under the curve (camera, 0.86; film digitizer, 0.91, p = 0.29). The digital camera set cost 965 dollars compared to 3000 dollars for the film digitizer. CONCLUSION: Detection of pneumonia was not measurably compromised by using digital cameras compared with film digitizers. The 3-fold lower cost of the digital camera makes this technology an affordable and widely accessible alternative for surveillance systems, vaccine trials, and perhaps clinical use.

Diagnosis, Computer-Assisted↗

PubMed2005

Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach.

Monitoring of uterine contraction activity is an important diagnostic tool used during both pregnancy and labour. The strain the pregnant uterus exerts on the maternal abdomen is measured via external tocography. However, limitation of this approach has caused the development of another technique-electrohysterography--which is based on the recording of electrical uterine activity. A computer-aided system is presented, which allows the recording of electrohysterographic signals from the maternal abdomen and their on-line analysis both in time and frequency domains. As a research material, we acquired 108 traces during a 24 h period before labour from a group of patients between 37 and 40 weeks of gestation. The comparison study between electrohysterography and tocography was carried out thanks to the possibility of simultaneous recording of mechanical and electrical uterine activities. The obtained results show that both methods demonstrate high agreement in relation to the number of contractions recognized as being consistent. However, their agreement in relation to the quantitative description of recognized patterns has appeared to be unacceptable to consider these methods as fully alternative. The appropriate way of further development of electrohysterography seems to be spectral analysis. Several spectral parameters describing electrophysiological properties of uterine muscle can be obtained by the use of electrohysterographic signals.

Diagnosis, Computer-Assisted↗