Computational Intelligence in Medical Imaging

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advanced medical image classification
artificial neural networks
Atanassov's Intuitionistic Fuzzy Set
Atanassov’s Intuitionistic Fuzzy Set
biomedical image analysis
Border Irregularity
cancer analyiss
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computational intelligence
Computational Intelligence Techniques
Data Set
Deformable Models
deformable organisms
emission tomography reconstruction
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evolutionary algorithms
fuzzy logic methods
GAs
Ground Truth Images
Image Segmentation
Intersubject Registration
Intuitionistic Fuzzy Sets
Mammographic Image
Medical Image Processing
Medical Image Segmentation
medical imaging
Membership Function
MIA
monte carlo simulations
neural networks
prostate cancer detection
RL Agent
Rough Sets
Rough Sets Approach
Rough Sets Theory
Septal Penetration
SVM
Transform Parameters
TRE
TS

Product details

  • ISBN 9781138112209
  • Weight: 940g
  • Dimensions: 156 x 234mm
  • Publication Date: 12 Sep 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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CI Techniques & Algorithms for a Variety of Medical Imaging SituationsDocuments recent advances and stimulates further research

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.

The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary optimization techniques, rough sets, support vector machines, tabu search, fuzzy logic, a Bayesian probabilistic framework, a statistical parts-based appearance model, a reinforcement learning-based multistage image segmentation algorithm, a machine learning approach, Monte Carlo simulations, and intelligent, deformable models. The contributors discuss how these techniques are used to classify wound images, extract the boundaries of skin lesions, analyze prostate cancer, handle the inherent uncertainties in mammographic images, and encapsulate the natural intersubject anatomical variance in medical images. They also examine prostate segmentation in transrectal ultrasound images, automatic segmentation and diagnosis of bone scintigraphy, 3-D medical image segmentation, and the reconstruction of SPECT and PET tomographic images.

G. Schaefer, A. Hassanien, J. Jiang