15 Result(s)
within
Computer Science
Imaging / Radiology
Pattern Recognition
Zhuo** **e
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Chapter and Conference Paper
Dense Volume-to-Volume Vascular Boundary Detection
In this work, we tackle the important problem of dense 3D volume labeling in medical imaging. We start by introducing HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). Next, we develop a n...
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Chapter and Conference Paper
A Stochastic Quasi-Newton Method for Non-Rigid Image Registration
Image registration is often very slow because of the high dimensionality of the images and complexity of the algorithms. Adaptive stochastic gradient descent (ASGD) outperforms deterministic gradient descent a...
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Chapter and Conference Paper
Structural Edge Detection for Cardiovascular Modeling
Computational simulations provide detailed hemodynamics and physiological data that can assist in clinical decision-making. However, accurate cardiovascular simulations require complete 3D models constructed f...
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Chapter and Conference Paper
Visual Phrase Learning and Its Application in Computed Tomographic Colonography
In this work, we propose a visual phrase learning scheme to learn an optimal visual composite of anatomical components/parts from CT colonography images for computer-aided detection. The key idea is to utilize...
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Chapter and Conference Paper
Sequential Monte Carlo Tracking for Marginal Artery Segmentation on CT Angiography by Multiple Cue Fusion
In this work we formulate vessel segmentation on contrast-enhanced CT angiogram images as a Bayesian tracking problem. To obtain posterior probability estimation of vessel location, we employ sequential Monte ...
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Chapter and Conference Paper
Context-Constrained Multiple Instance Learning for Histopathology Image Segmentation
Histopathology image segmentation plays a very important role in cancer diagnosis and therapeutic treatment. Existing supervised approaches for image segmentation require a large amount of high quality manual ...
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Chapter and Conference Paper
Teniae Coli Extraction in Human Colon for Computed Tomographic Colonography Images
Teniae coli are three bands of longitudinal smooth muscle on the surface of the colon, serving as anatomically meaningful landmarks for guiding virtual colonoscopic navigation and registration. This paper pres...
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Chapter and Conference Paper
Robust Skull Strip** of Clinical Glioblastoma Multiforme Data
Skull strip** is the first step in many neuroimaging analyses and its success is critical to all subsequent processing. Methods exist to skull strip brain images without gross deformities, such as those affe...
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Chapter and Conference Paper
Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning
This paper presents a new supervised learning framework for the efficient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Trainin...
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Chapter and Conference Paper
Classification of Alzheimer’s Disease Using a Self-Smoothing Operator
In this study, we present a system for Alzheimer’s disease classification on the ADNI dataset [1]. Our system is able to learn/fuse registration-based (matching) and overlap-based similarity measures, which ar...
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Chapter and Conference Paper
Automatic Subcortical Segmentation Using a Contextual Model
Automatically segmenting subcortical structures in brain images has the potential to greatly accelerate drug trials and population studies of disease. Here we propose an automatic subcortical segmentation algo...
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Chapter and Conference Paper
Joint Sulci Detection Using Graphical Models and Boosted Priors
In this paper we propose an automated approach for joint sulci detection on cortical surfaces by using graphical models and boosting techniques to incorporate shape priors of major sulci and their Markovian re...
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Chapter and Conference Paper
Towards Whole Brain Segmentation by a Hybrid Model
Segmenting cortical and sub-cortical structures from 3D brain images is of significant practical importance. However, various anatomical structures have similar intensity patterns in MRI, and the automatic seg...
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Chapter and Conference Paper
Segmentation of Sub-cortical Structures by the Graph-Shifts Algorithm
We propose a novel algorithm called graph-shifts for performing image segmentation and labeling. This algorithm makes use of a dynamic hierarchical representation of the image. This representation allows each ...
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Chapter and Conference Paper
A Learning Based Algorithm for Automatic Extraction of the Cortical Sulci
This paper presents a learning based method for automatic extraction of the major cortical sulci from MRI volumes or extracted surfaces. Instead of using a few pre-defined rules such as the mean curvature prop...
