scholarly journals A Novel Two-Dimensional Echocardiographic Image Analysis System Using Artificial Intelligence-Learned Pattern Recognition for Rapid Automated Ejection Fraction

2007 ◽  
Vol 49 (2) ◽  
pp. 217-226 ◽  
Author(s):  
Maxime Cannesson ◽  
Masaki Tanabe ◽  
Matthew S. Suffoletto ◽  
Dennis M. McNamara ◽  
Shobhit Madan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Ejection Fraction ◽  
Image Analysis System ◽  
Two Dimensional ◽  
Echocardiographic Image ◽  
Analysis System

Sequential topographical portrayal of myocardial blood flow

1989 ◽  
Vol 257 (2) ◽  
pp. H635-H642
Author(s):  
J. F. Richeson ◽  
R. C. Waag ◽  
D. Zwierzynski ◽  
E. A. Schenk
Keyword(s):  
Blood Flow ◽  
Image Analysis ◽  
Myocardial Blood Flow ◽  
Ischemic Injury ◽  
Radioactive Isotopes ◽  
Image Analysis System ◽  
Two Dimensional ◽  
Flow Maps ◽  
Computer Based ◽  
Analysis System

Methods to portray myocardial blood flow in a two-dimensional continuum are advantageous in that they allow blood flow history to be overlaid on histological or histochemical descriptions of the consequences of ischemia. We describe here autoradiographic methods that allow such portrayals at three separate times during the evolution of ischemic injury. A computer-based image-analysis system was used to derive such flow maps by taking advantage of the physical characteristics of radioactive isotopes.

Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies

2019 ◽  
Vol 8 (4) ◽  
pp. 38-54 ◽  
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
George Valasoulis ◽  
Nektarios Koufopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
System Performance ◽  
Malignant Disease ◽  
Competitive Learning ◽  
Image Analysis System ◽  
Morphometric Features ◽  
Analysis System ◽  
Histological Confirmation

Objective of this study is to investigate the potential of an artificial intelligence (AI) technique, based on competitive learning, for the discrimination of benign from malignant endometrial nuclei and lesions. For this purpose, 416 liquid-based cytological smears with histological confirmation were collected, each smear corresponded to one patient. From each smear was extracted nuclear morphometric features by the application of an image analysis system. Subsequently nuclei measurement from 50% of the cases were used to train the AI system to classify each individual nucleus as benign or malignant. The remaining measurement, from the unused 50% of the cases, were used for AI system performance evaluation. Based on the results of nucleus classification the patients were discriminated as having benign or malignant disease by a secondary subsystem specifically trained for this purpose. Based on the results it was conclude that AI based computerized systems have the potential for the classification of both endometrial nuclei and lesions.

Chemical Classification and Particle Sizing of Foundry Sands

1985 ◽  
Vol 43 ◽  
pp. 388-389
Author(s):  
D.S. DeMiglio
Keyword(s):  
Image Analysis ◽  
Energy Dispersive Spectrometer ◽  
Image Analysis System ◽  
Representative Sampling ◽  
Foundry Sands ◽  
Large Atomic Number ◽  
Backscattered Electron ◽  
Sand Particles ◽  
Analysis System ◽  
Reclamation System

Much progress has been made in recent years towards the development of closed-loop foundry sand reclamation systems. However, virtually all work to date has determined the effectiveness of these systems to remove surface clay and metal oxide scales by a qualitative inspection of a representative sampling of sand particles. In this investigation, particles from a series of foundry sands were sized and chemically classified by a Lemont image analysis system (which was interfaced with an SEM and an X-ray energy dispersive spectrometer) in order to statistically document the effectiveness of a reclamation system developed by The Pangborn Company - a subsidiary of SOHIO.The following samples were submitted: unreclaimed sand; calcined sand; calcined & mechanically scrubbed sand and unused sand. Prior to analysis, each sample was sprinkled onto a carbon mount and coated with an evaporated film of carbon. A backscattered electron photomicrograph of a field of scale-covered particles is shown in Figure 1. Due to a large atomic number difference between sand particles and the carbon mount, the backscattered electron signal was used for image analysis since it had a uniform contrast over the shape of each particle.

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