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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An Interactive X-Ray Image Segmentation Technique for Bone Extraction
TL;DR: The experimental results on real X-rays show that the proposed segmentation algorithm is highly eective, since it has the ability to extract the contour of the desired objects from the image.
Journal ArticleDOI
A new approach for zoning irregularly-spaced, within-field data
TL;DR: This work proposes a new approach to generate contiguous management zones from irregularly-spaced within- field observations, e.g. within-field yield, soil conductivity, soil samples, which are a very important source of data in precision agriculture studies.
Proceedings ArticleDOI
Weighted Ensemble of Deep Learning Models based on Comprehensive Learning Particle Swarm Optimization for Medical Image Segmentation
TL;DR: Wang et al. as discussed by the authors proposed a weighted ensemble method in which the weighted sum of segmentation outputs by classifiers is used to choose the final segmentation decision, and they used a swarm intelligence algorithm namely Comprehensive Learning Particle Swarm Optimization to optimize the combining weights.
Journal ArticleDOI
SinGAN-Seg: Synthetic training data generation for medical image segmentation
TL;DR: In this article , the authors presented a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image, which is different from the traditional generative adversarial networks (GANs).
Proceedings ArticleDOI
Fuzzy c-means with wavelet filtration for MR image segmentation
TL;DR: An image segmentation technique based on fuzzy c-means (FCM) incorporated with wavelet domain noise filtration that outperforms other FCM variations, in terms of quantitative performance measure and visual quality.
References
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Book
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.