13 Result(s)
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Chapter and Conference Paper
Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction
Hypermethylation of the O6-methylguanine-DNA-methyltransferase (MGMT) promoter in glioblastoma (GBM) is a predictive biomarker associated with improved treatment outcome. In clinical practice, MGMT methylation...
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Chapter and Conference Paper
Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features
We define and investigate the Local Rotation Invariance (LRI) and Directional Sensitivity (DS) of radiomics features. Most of the classical features cannot combine the two properties, which are antagonist in s...
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Chapter and Conference Paper
Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting
Medical experts commonly use imaging including Computed Tomography (CT), Positron-Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) for diagnosis or to plan a surgery. These scans give a highly de...
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Chapter and Conference Paper
A Lung Graph–Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images
This article presents a novel graph–model approach encoding the relations between the perfusion in several regions of the lung extracted from a geometry–based atlas. Unlike previous approaches that individuall...
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Chapter
Text- and Content-Based Medical Image Retrieval in the VISCERAL Retrieval Benchmark
Text- and content-based retrieval are the most widely used approaches for medical image retrieval. They capture the similarity between the images from different perspectives: text-based methods rely on manual ...
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Chapter and Conference Paper
Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment
The number density of scatterers in tumor tissue contribute to a heterogeneous ultrasound speckle pattern that can be difficult to discern by visual observation. Such tumor stochastic behavior becomes even mor...
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Chapter and Conference Paper
Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma
Medulloblastoma (MB) is a type of brain cancer that represent roughly 25% of all brain tumors in children. In the anaplastic medulloblastoma subtype, it is important to identify the degree of irregularity and ...
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Chapter and Conference Paper
USYD/HES-SO in the VISCERAL Retrieval Benchmark
This report presents the participation of our joint research team in the VISCERAL retrieval task. Given a query case, the cases with highest similarities in the database were retrieved. 5 runs were submitted f...
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Chapter and Conference Paper
Epileptogenic Lesion Quantification in MRI Using Contralateral 3D Texture Comparisons
Epilepsy is a disorder of the brain that can lead to acute crisis and temporary loss of brain functions. Surgery is used to remove focal lesions that remain resistant to treatment. An accurate localization of ...
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Chapter and Conference Paper
Multiscale Lung Texture Signature Learning Using the Riesz Transform
Texture–based computerized analysis of high–resolution computed tomography images from patients with interstitial lung diseases is introduced to assist radiologists in image interpretation. The cornerstone of ...
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Article
Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows
Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed.
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Chapter and Conference Paper
Lung Texture Classification Using Locally–Oriented Riesz Components
We develop a texture analysis framework to assist radiologists in interpreting high–resolution computed tomography (HRCT) images of the lungs of patients affected with interstitial lung diseases (ILD). Novel t...
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Chapter and Conference Paper
Learning a Frequency–Based Weighting for Medical Image Classification
This article describes the use of a frequency–based weighting developed for image retrieval to perform automatic annotation of images (medical and non–medical). The techniques applied are based on a simple tf/idf
