Genetic algorithms applied to Fourier-descriptor-based geometric models for anatomical object recognition in medical images

1996 ◽  
Author(s):  
Kostas Delibasis ◽  
Peter E. Undrill ◽  
George G. Cameron
Keyword(s):  
Genetic Algorithms ◽  
Object Recognition ◽  
Medical Images ◽  
Fourier Descriptor ◽  
Geometric Models

A mirror reflection and aspect ratio invariant approach to object recognition using Fourier descriptor

2011 ◽  
Vol 11 (5) ◽  
pp. 3910-3915 ◽  
Author(s):  
Maruti Agarwal ◽  
Vikram Venkatraghavan ◽  
Chandan Chakraborty ◽  
Ajoy K. Ray
Keyword(s):  
Object Recognition ◽  
Aspect Ratio ◽  
Mirror Reflection ◽  
Fourier Descriptor ◽  
Invariant Approach

scholarly journals Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network

2020 ◽  
Vol 3 (2) ◽  
pp. 258-265
Author(s):  
Al-Khowarizmi Al-Khowarizmi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Object Recognition ◽  
Success Rate ◽  
Model Classification ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better

Genetic Adaptation of Level Sets Parameters for Medical Imaging Segmentation

2010 ◽  
pp. 150-166
Author(s):  
Dário A.B. Oliveira ◽  
Raul Q. Feitosa ◽  
Mauro M. Correia
Keyword(s):  
Genetic Algorithms ◽  
Medical Imaging ◽  
Computer Tomography ◽  
Level Sets ◽  
Medical Images ◽  
Poor Performance ◽  
Initial Solution ◽  
Genetic Adaptation ◽  
Gaussian Curve ◽  
Measures Of Performance

This chapter presents a method based on level sets to segment organs using computer tomography (CT) medical images. Initially, the organ boundary is manually set in one slice as an initial solution, and then the method automatically segments the organ in all other slices, sequentially. In each step of iteration it fits a Gaussian curve to the organ’s slice histogram to model the speed image in which the level sets propagate. The parameters of our method are estimated using genetic algorithms (GA) and a database of reference segmentations. The method was tested to segment the liver using 20 different exams and five different measures of performance, and the results obtained confirm the potential of the method. The cases in which the method presented a poor performance are also discussed in order to instigate further research.

Sign in / Sign up

Export Citation Format

Share Document