Audrey G. Chung

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Prostate cancer is the most diagnosed form of cancer, but survival rates are relatively high with sufficiently early diagnosis. Current computer-aided image-based cancer detection methods face notable challenges include noise in MRI images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult(More)
We conducted a qualitative study to explore the pathways by which traditional gender roles may ultimately affect Vietnamese women's interpretation of sexually transmitted disease (STD) symptoms and health-seeking strategies. Data on gender roles, perceptions of types of sexual relationships, perceptions of persons with STDs, and STD patient experiences were(More)
—The use of high volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the auto-detection of prostate tumours since it provides a plethora of mineable data which can be used for both detection and prognosis of prostate cancer. While current voxel-resolution radiomics-driven(More)
Markov random fields (MRFs) and conditional random fields (CRFs) are influential tools in image modeling, particularly for applications such as image segmentation. Local MRFs and CRFs utilize local nodal interactions when modeling, leading to excessive smoothness on boundaries (i.e., the short-boundary bias problem). Recently, the concept of fully connected(More)
We present a novel non-contact photoplethysmographic (PPG) imaging system based on high-resolution video recordings of ambient reflectance of human bodies that compensates for body motion and takes advantage of skin erythema fluctuations to improve measurement reliability for the purpose of remote heart rate monitoring. A single measurement location for(More)
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by(More)
—Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite these statistics, prognosis is relatively good with a sufficiently early diagnosis, making fast and reliable prostate cancer detection crucial. As imaging-based prostate cancer screening, such as magnetic resonance imaging (MRI),(More)
Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this(More)
Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this(More)
The reconstruction of high dynamic range (HDR) images via conventional camera systems and low dynamic range (LDR) images is a growing field of research in image acquisition. The radiance map associated with the HDR image of a scene is typically computed using multiple images of the same scene captured at different exposures (i.e., bracketed LDR imzages).(More)