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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Three Generations of Medical Image Segmentation: Methods and Available Software
Daniel Withey,Zoltan J. Koles +1 more
TL;DR: The progress toward fullyautomatic segmentation is discussed and sources of segmentation software from industry and academia are identified, along with databases for segmentation validation.
Journal ArticleDOI
Automating image segmentation verification and validation by learning test oracles
TL;DR: A framework referred to as Image Segmentation Automated Oracle (ISAO) is proposed that uses machine learning to construct an oracle, which can then be used to automatically verify the correctness of image segmentations, thus saving substantial resources and making the image segmentation verification and validation task significantly more efficient.
Journal ArticleDOI
A Review of Medical Imaging Informatics
Usha Sinha,Alex A. T. Bui,Ricky K. Taira,John David N. Dionisio,Craig A. Morioka,David W. Johnson,Hooshang Kangarloo +6 more
TL;DR: This review of medical imaging informatics is a survey of current developments in an exciting field, and reviews current multimedia visualization methods including temporal modeling, problem‐specific data organization, including the authors' problem‐centric, context and user‐specific visualization interface.
Patent
Methods, apparatuses, and computer program products for controlling luminance of non-tissue objects within an image
TL;DR: In this paper, a method for controlling the luminance of non-tissue objects within an image is described, based on one or more seed pixel values for an image and a mask pixel value based on defined luminance values.
Posted Content
Capsules for Biomedical Image Segmentation
TL;DR: This work expands the use of capsule networks to the task of object segmentation for the first time in the literature via the introduction of locally-constrained routing and transformation matrix sharing, which reduces the parameter/memory burden and allows for the segmentation of objects at large resolutions.
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.
Book
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.
Book
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.