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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.

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Citations
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Journal ArticleDOI

Quantitative image analysis: software systems in drug development trials.

TL;DR: A case study is presented using a prototype system that is used for quantifying multiple sclerosis lesions from multivariate MRI.
Journal ArticleDOI

Flow stagnation volume and abdominal aortic aneurysm growth: Insights from patient-specific computational flow dynamics of Lagrangian-coherent structures.

TL;DR: This study reveals that transient transport topology can be quantified in patient-specific AAA during disease progression by CTA, in parallel with lumen morphology, and is anticipated that analysis of the main AAA stagnation zones by patient- specific CFD on a yearly basis could help to predict AAA growth and rupture.
Journal ArticleDOI

Deep learning in electron microscopy

TL;DR: This review paper offers a practical perspective aimed at developers with limited familiarity of deep learning in electron microscopy that discusses hardware and software needed to get started with deep learning and interface with electron microscopes.
Proceedings ArticleDOI

Threshold-Based Image Segmentation through an Improved Particle Swarm Optimisation

TL;DR: A new multilevel threshold-based image segmentation approach on the basis of the new particle swarm optimisation with wavelet mutation that leads to better optimised thresholds and produces more accurate segmentation results for images with multiple attributes.
Journal ArticleDOI

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation

TL;DR: This paper proposes a novel anatomy-regularized representation learning approach for segmentation-oriented cross-modality image synthesis, which learns a common feature encoding across different modalities to form a shared latent space.
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

Simon Haykin
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

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.
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