50 Result(s)
within
Yen-Wei Chen
Health Informatics
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Chapter and Conference Paper
MR-CT Image Registration in Liver Cancer Treatment with an Open Configuration MR Scanner
MR – CT image registration has been used in the liver cancer treatment with an open MR Scanner to guide percutaneous puncture for ablation of tumors. Due to low magnetic field and limited acquisition time, MR ...
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Chapter and Conference Paper
Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multi-level Statistical Shape Model
An atlas-based automated liver segmentation method from 3D CT images is described. The method utilizes two types of atlases, that is, the probabilistic atlas (PA) and statistical shape model (SSM). Voxel-based...
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Chapter and Conference Paper
Utilizing Disease-Specific Organ Shape Components for Disease Discrimination: Application to Discrimination of Chronic Liver Disease from CT Data
We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace rep...
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Article
Capturing large shape variations of liver using population-based statistical shape models
Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect paramete...
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Chapter and Conference Paper
HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor
This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient...
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Chapter and Conference Paper
HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs
This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the...
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Article
Improved segmentation of low-contrast lesions using sigmoid edge model
The intensity profile of an image in the vicinity of a tissue’s boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue texture...
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Chapter and Conference Paper
Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification
Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First...
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Chapter and Conference Paper
Self-taught Learning with Residual Sparse Autoencoders for HEp-2 Cell Staining Pattern Recognition
Self-taught learning aims at obtaining compact and latent representations from data them-selves without previously manual labeling, which would be time-consuming and laborious. This study proposes a novel self...
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Chapter and Conference Paper
Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-phase CT Images
Computer-aided diagnosis (CAD) systems are useful for assisting radiologists with clinical diagnoses by classifying focal liver lesions (FLLs) based on multi-phase computed tomography (CT) images. Although man...
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Article
Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images
The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a ...
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Chapter and Conference Paper
A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images
Focal liver lesion classification is important to the diagnostics of liver disease. In clinics, lesion type is usually determined by multi-phase contrast-enhanced CT images. Previous methods of automatic live...
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Chapter and Conference Paper
Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior
Medical image segmentation is one of the most important steps in computer-aided intervention and diagnosis. Although deep learning-based segmentation methods have achieved great success in computer vision doma...
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Chapter
Improving the Performance of Deep CNNs in Medical Image Segmentation with Limited Resources
Convolutional neural networks (CNNs) have obtained enormous success in image segmentation, which is substantial in many clinical treatments. Even though CNNs have achieved state-of-the-art performances, most r...
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Chapter and Conference Paper
Dynamic Facial Features in Positive-Emotional Speech for Identification of Depressive Tendencies
Depressive symptoms in young people may persist into adulthood and develop into depression. Early screening of depressive tendencies in university students helps to reduce the number and intensity of their dep...
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Chapter and Conference Paper
A 3D Shrinking-and-Expanding Module with Channel Attention for Efficient Deep Learning-Based Super-Resolution
The 3-dimensional (3D) super-resolution (SR) for medical volumetric data is confirmed to provide better visual results compared to conventional 2-dimensional processing. Then, considering practical application...
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Chapter and Conference Paper
Hand-Crafted and Deep Learning-Based Radiomics Models for Recurrence Prediction of Non-Small Cells Lung Cancers
This research was created to examine the recurrence of non-small lung cancer (NSCLC) using computed-tomography images (CT-images) to avoid biopsy from patients because the cancer cells may have an uneven distr...
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Chapter
Medical Image Classification Using Deep Learning
Image classification is to assign one or more labels to an image, which is one of the most fundamental tasks in computer vision and pattern recognition. In traditional image classification, low-level or mid-le...
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Chapter
Multi-scale Deep Convolutional Neural Networks for Emphysema Classification and Quantification
In this work, we aim at classification and quantification of emphysema in computed tomography (CT) images of lungs. Most previous works are limited to extracting low-level features or mid-level features withou...
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Chapter
Medical Image Enhancement Using Deep Learning
This chapter aims to introduce medical image enhancement technology using 2-dimentional and 3-dimentional deep learning. The article starts from basic methods about convolutional layer, deconvolution layer, lo...
