Dr. Sun Young Park Profile

Dr. Sun Young Park

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Publications (7)

Proceedings Article | 13 March 2019 Presentation + Paper

Breast density follow-up decision support system using deep convolutional models

Sun Young Park, Dustin Sargent, David Richmond

Proceedings Volume 10950, 109500G (2019) https://doi.org/10.1117/12.2513047

KEYWORDS: Breast, Data modeling, Image classification, Binary data, Decision support systems, Breast cancer, Tissues, Mammography

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Proceedings Article | 24 February 2017 Presentation + Paper

Tumor propagation model using generalized hidden Markov model

Sun Young Park, Dustin Sargent

Proceedings Volume 10133, 101331G (2017) https://doi.org/10.1117/12.2254583

KEYWORDS: Tissues, Image segmentation, Image analysis, Image processing, Medical imaging, Diagnostics, Lung, Tumors, Detection and tracking algorithms, 3D modeling, Image processing algorithms and systems, Spherical lenses, Image registration

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Proceedings Article | 24 February 2017 Paper

Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

Dustin Sargent, Sun Young Park

Proceedings Volume 10133, 101332R (2017) https://doi.org/10.1117/12.2254575

KEYWORDS: Image segmentation, Lung, Computer programming, Tissues, Chest, Natural surfaces, Spherical lenses, Machine vision, Computer vision technology, Shape analysis, Error analysis, Tumors, 3D metrology, Detection and tracking algorithms

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Proceedings Article | 24 March 2016 Paper

Colonoscopic polyp detection using convolutional neural networks

Sun Young Park, Dusty Sargent

Proceedings Volume 9785, 978528 (2016) https://doi.org/10.1117/12.2217148

KEYWORDS: Neural networks, Image registration, Feature extraction, Luminescence, Video, Image processing, Colorectal cancer, Gold, Detection and tracking algorithms, Image classification, Convolutional neural networks, Diagnostics, Computer aided diagnosis and therapy, Tissues, Medical imaging, Solid modeling, Colon, Convolution

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Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician’s interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report testing results for the new algorithm using both human and mouse colonoscopy data.

Proceedings Article | 21 March 2016 Paper

Automatic segmentation of mammogram and tomosynthesis images

Dusty Sargent, Sun Young Park

Proceedings Volume 9784, 978437 (2016) https://doi.org/10.1117/12.2217123

KEYWORDS: Image segmentation, Mammography, Image processing algorithms and systems, Breast, Image processing, Image registration, Skin, Tissues, Shape analysis, Image restoration, Breast cancer, Digital mammography, Cancer, 3D image processing

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Breast cancer is a one of the most common forms of cancer in terms of new cases and deaths both in the United States and worldwide. However, the survival rate with breast cancer is high if it is detected and treated before it spreads to other parts of the body. The most common screening methods for breast cancer are mammography and digital tomosynthesis, which involve acquiring X-ray images of the breasts that are interpreted by radiologists. The work described in this paper is aimed at optimizing the presentation of mammography and tomosynthesis images to the radiologist, thereby improving the early detection rate of breast cancer and the resulting patient outcomes. Breast cancer tissue has greater density than normal breast tissue, and appears as dense white image regions that are asymmetrical between the breasts. These irregularities are easily seen if the breast images are aligned and viewed side-by-side. However, since the breasts are imaged separately during mammography, the images may be poorly centered and aligned relative to each other, and may not properly focus on the tissue area. Similarly, although a full three dimensional reconstruction can be created from digital tomosynthesis images, the same centering and alignment issues can occur for digital tomosynthesis. Thus, a preprocessing algorithm that aligns the breasts for easy side-by-side comparison has the potential to greatly increase the speed and accuracy of mammogram reading. Likewise, the same preprocessing can improve the results of automatic tissue classification algorithms for mammography. In this paper, we present an automated segmentation algorithm for mammogram and tomosynthesis images that aims to improve the speed and accuracy of breast cancer screening by mitigating the above mentioned problems. Our algorithm uses information in the DICOM header to facilitate preprocessing, and incorporates anatomical region segmentation and contour analysis, along with a hidden Markov model (HMM) for processing the multi-frame tomosynthesis images. The output of the algorithm is a new set of images that have been processed to show only the diagnostically relevant region and align the breasts so that they can be easily compared side-by-side. Our method has been tested on approximately 750 images, including various examples of mammogram, tomosynthesis, and scanned images, and has correctly segmented the diagnostically relevant image region in 97% of cases.

Proceedings Article | 9 March 2011 Paper

Colonoscopy video quality assessment using hidden Markov random fields

Sun Young Park, Dusty Sargent, Inbar Spofford, Kirby Vosburgh

Proceedings Volume 7963, 79632P (2011) https://doi.org/10.1117/12.878217

KEYWORDS: Video, Diagnostics, Colorectal cancer, Quality measurement, Data modeling, Image quality, Image information entropy, Colon, Endoscopy, Cancer

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Proceedings Article | 2 March 2011 Paper

Image-based camera motion estimation using prior probabilities

Dusty Sargent, Sun Young Park, Inbar Spofford, Kirby Vosburgh

Proceedings Volume 7964, 79641U (2011) https://doi.org/10.1117/12.878244

KEYWORDS: Cameras, Motion estimation, Endoscopes, Motion models, Colon, Sensors, Detection and tracking algorithms, Video, Magnetism, Magnetic tracking

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Image-based camera motion estimation from video or still images is a difficult problem in the field of computer vision. Many algorithms have been proposed for estimating intrinsic camera parameters, detecting and matching features between images, calculating extrinsic camera parameters based on those features, and optimizing the recovered parameters with nonlinear methods. These steps in the camera motion inference process all face challenges in practical applications: locating distinctive features can be difficult in many types of scenes given the limited capabilities of current feature detectors, camera motion inference can easily fail in the presence of noise and outliers in the matched features, and the error surfaces in optimization typically contain many suboptimal local minima. The problems faced by these techniques are compounded when they are applied to medical video captured by an endoscope, which presents further challenges such as non-rigid scenery and severe barrel distortion of the images. In this paper, we study these problems and propose the use of prior probabilities to stabilize camera motion estimation for the application of computing endoscope motion sequences in colonoscopy. Colonoscopy presents a special case for camera motion estimation in which it is possible to characterize typical motion sequences of the endoscope. As the endoscope is restricted to move within a roughly tube-shaped structure, forward/backward motion is expected, with only small amounts of rotation and horizontal movement. We formulate a probabilistic model of endoscope motion by maneuvering an endoscope and attached magnetic tracker through a synthetic colon model and fitting a distribution to the observed motion of the magnetic tracker. This model enables us to estimate the probability of the current endoscope motion given previously observed motion in the sequence. We add these prior probabilities into the camera motion calculation as an additional penalty term in RANSAC to help reject improbable motion parameters caused by outliers and other problems with medical data. This paper presents the theoretical basis of our method along with preliminary results on indoor scenes and synthetic colon images.

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