Journal ArticleDOI
Current methods in medical image segmentation.
TLDR
A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.Abstract:
▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semiautomated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.read more
Citations
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Book ChapterDOI
Medical Image Segmentation Based On Deformable Models And Its Applications
Yonggang Wang,Qiang Guo,Yun Zhu +2 more
TL;DR: This chapter discusses medical image segmentation based on deformable models and its applications, and three applications in different medical fields are introduced: tongue image segmentsation in Chinese medicine, cerebral cortex segmentation in MR images, and cardiac valve segmentations in echocardiographic sequences.
Journal ArticleDOI
Mammography and Computerized Decision Systems
TL;DR: Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels, which leads to the conclusion that CAD Systems can be incorporated into clinical practice as a double reading option to radiologists.
Journal ArticleDOI
Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations
TL;DR: A new efficient image denoising scheme is proposed, which introduces a sparse term to reduce non-Gaussian outliers from multiplicative noise and uses a Laplacian Schatten norm to capture the global structure information.
Book ChapterDOI
Biomedical Imaging Informatics
TL;DR: After reading this chapter, you should know the answers to these questions: what makes images a challenging type of data to be processed by computers when compared to non-image clinical data.
Journal ArticleDOI
Software agent with reinforcement learning approach for medical image segmentation
Mahsa Chitsaz,Chaw-Seng Woo +1 more
TL;DR: A self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images using a learning phase that is based on reinforcement learning (RL) system.
References
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Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
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Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging
TL;DR: Direct and Indirect Radiologic Localization Reference System: Basal Brain Line CA-CP Cerebral Structures in Three-Dimensional Space Practical Examples for the Use of the Atlas in Neuroradiologic Examinations Three- Dimensional Atlas of a Human Brain Nomenclature-Abbreviations Anatomic Index Conclusions.