Proceedings ArticleDOI
The image recognition of brain-stem ultrasound images with using a neural network based on PCA
Jiri Blahuta,Tomáš Soukup,Petr Cermak +2 more
- pp 137-142
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TLDR
In this paper, the authors described how to recognize substantia nigra (SN) area in ultrasound brain-stem images using artificial neural networks and solved the problem with MATLAB, with Image Processing and Neural Network Toolboxes.Abstract:
This paper describes how to recognize substantia nigra (SN) area in ultrasound brain-stem images. The main goal is the classification of ROI SN in midbrain. The classification of images is useful to detection Parkinson's disease (PD), defects in SN. Work is based on image processing and is realized with the help of artificial neural networks and solve is realized with MATLAB, with Image Processing and Neural Network Toolboxes.read more
Citations
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Journal ArticleDOI
Classification of Ultrasound Kidney Images using PCA and Neural Networks
TL;DR: A computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases and a correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.
Journal ArticleDOI
Developments in the Role of Transcranial Sonography for the Differential Diagnosis of Parkinsonism
TL;DR: Recent evidence supporting TCS as a reliable method in the differential diagnosis of parkinsonism, combining substantia nigra (SN), basal ganglia and ventricular system findings is highlighted.
Journal ArticleDOI
A new program for highly reproducible automatic evaluation of the substantia nigra from transcranial sonographic images
Jiri Blahuta,Tomáš Soukup,Monika Jelínková,Petra Bártová,Petr Cermak,Roman Herzig,David Školoudík +6 more
TL;DR: Test the reliability of the data using developed B-Mode Assist software in patients with parkinsonism and in healthy volunteers shows very reliable measurement of SN features using designed application with "almost perfect" inter-observer and intra-ob server agreements.
Proceedings Article
Ultrasound medical image recognition with artificial intelligence for Parkinson's disease classification
TL;DR: This paper shows how to classify the medical ultrasound images by using artificial intelligence with experimental software with MATLAB for a classification of ROI substantia nigra in midbrain which is useful to detection Parkinson's disease.
ROC and reproducibility analysis of designed algorithm for potential diagnosis of Parkinson's disease in ultrasound images
TL;DR: Experimental software which has been developed in MATLAB for potential detection of pathology to detection of Parkinson's disease and statistical analysis of these data such as ROC curve, variability and Cohen's kappa coefficient is introduced.
References
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Using multivariate statistics
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
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Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
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Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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Applied Statistics and Probability for Engineers
TL;DR: Montgomery and Runger's Engineering Statistics text as discussed by the authors provides a practical approach oriented to engineering as well as chemical and physical sciences by providing unique problem sets that reflect realistic situations, students learn how the material will be relevant in their careers.
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Diagnostic Criteria for Parkinson Disease
TL;DR: A clinical diagnostic classification based on a comprehensive review of the literature regarding the sensitivity and specificity of the characteristic clinical features of PD is proposed: Definite, Probable, and Possible.