JungHwan Oh

University of North Texas, Denton, Texas, United States

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Publications (39)97.03 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician׳s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a “texton histogram” of an image block as features. The histogram captures the distribution of different “textons” representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.
    Neurocomputing 11/2014; 144:70–91. DOI:10.1016/j.neucom.2014.02.064 · 2.01 Impact Factor
  • Gastroenterology 05/2014; 146(5):S-728-S-729. DOI:10.1016/S0016-5085(14)62641-X · 13.93 Impact Factor
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    ABSTRACT: This paper presents the first fully automated reconstruction technique of 3D virtual colon segments from individual colonoscopy images. It is the basis of new software applications that may offer great benefits for improving quality of care for colonoscopy patients. For example, a 3D map of the areas inspected and uninspected during colonoscopy can be shown on request of the endoscopist during the procedure. The endoscopist may revisit the suggested uninspected areas to reduce the chance of missing polyps that reside in these areas. The percentage of the colon surface seen by the endoscopist can be used as a coarse objective indicator of the quality of the procedure. The derived virtual colon models can be stored for post-procedure training of new endoscopists to teach navigation techniques that result in a higher level of procedure quality. Our technique does not require a prior CT scan of the colon or any global positioning device. Our experiments on endoscopy images of an Olympus synthetic colon model reveal encouraging results with small average reconstruction errors (4.1mm for the fold depths and 12.1mm for the fold circumferences).
    Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 10/2013; DOI:10.1016/j.compmedimag.2013.10.005 · 1.04 Impact Factor
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    ABSTRACT: This paper presents a novel technique for automated detection of protruding polyps in colonoscopy images using edge cross-section profiles (ECSP). We propose a part-based multi-derivative ECSP that computes derivative functions of an edge cross-section profile and segments each of these profiles into parts. Therefore, we can model or extract features suitable for each part. Our features obtained from the parts can effectively describe complex properties of protruding polyps including the shape of the parts, texture, and protrusion and smoothness of polyp surface. We evaluated our method against two existing polyp image detection techniques on 42 different polyps, including those with little protrusion. Each polyp has a large variation of appearance in viewing angles, light conditions, and scales in different images. The evaluation showed that our technique outperformed the existing techniques in both accuracy and analysis time. Our method has a higher area under the free-response receiver operating characteristic curve. For instance, when both techniques have a true positive rate for polyp image detection of 81.4%, the average number of false regions per image of our technique is 0.32 compared to 1.8 of the best existing technique under study. Additionally, our technique can precisely mark edges of candidate polyp regions as visual feedback. These results altogether indicate that our technique is promising to provide visual feedback of polyp regions in clinical practice.
    10/2013; 18(4). DOI:10.1109/JBHI.2013.2285230
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    ABSTRACT: This paper describes the design and implementation of SAPPHIRE - a novel middleware and software development kit for stream programing on a heterogeneous system of multi-core multi-CPUs with optional hardware accelerators such as graphics processing unit (GPU). A stream program consists of a set of tasks where the same tasks are repeated over multiple iterations of data (e.g., video frames). Examples of such programs are video analysis applications for computer-aided diagnosis and computer-assisted surgeries. Our design goal is to reduce the implementation efforts and ease collaborative software development of stream programs while supporting efficient execution of the programs on the target hardware. To validate the toolkit, we implemented EM-Automated-RT software with the toolkit and reported our experience. EM-Automated-RT performs real-time video analysis for quality of a colonoscopy procedure and provides visual feedback to assist the endoscopist to achieve optimal inspection of the colon during the procedure. The software has been deployed in a hospital setting to conduct a clinical trial.
    Computer methods and programs in biomedicine 08/2013; 112(3). DOI:10.1016/j.cmpb.2013.07.028 · 1.56 Impact Factor
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    ABSTRACT: Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy helped to detect more polyps. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects the retroflexion during colonoscopy. We propose region shape and location (RSL) features and edgeless edge cross-section profile (ECSP) features that encapsulate important properties of endoscope appearance and edge information during retroflexion. Our experimental results on 50 colonoscopy test videos show that a simple ensemble classifier using both ECSP and RSL features can effectively identify retroflexion in terms of analysis time and detection rate.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 10/2012; 17(1). DOI:10.1109/TITB.2012.2226595 · 1.69 Impact Factor
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    ABSTRACT: Colonoscopy is the preferred screening method currently available for detection of colorectal cancer and its precursor lesions, colorectal polyps. However, recent data suggest that there is a significant miss rate for the detection of polyps in the colon during colonoscopy. Therefore, techniques for real-time quality measurement and feedback are necessary to aid the endoscopist towards optimal inspection to improve the overall quality of colonoscopy during the procedure. A typical colonoscopy procedure consists of two phases: an insertion phase and a withdrawal phase. One of the most essential tasks in real-time fully automated quality measurement is to find the location of the boundary between insertion and withdrawal phases. In this paper, we present a method based on motion vector templates to detect the phase boundary in real-time. The proposed method detects the phase boundary with a better accuracy and a faster speed compared to our previous method.
    Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications; 10/2012
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    ABSTRACT: Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.
    Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on; 07/2011
  • Gastroenterology 01/2011; 140(5). DOI:10.1016/S0016-5085(11)62982-X · 12.82 Impact Factor
  • Gastroenterology 01/2011; 140(5). DOI:10.1016/S0016-5085(11)62322-6 · 12.82 Impact Factor
  • Gastroenterology 05/2010; 138(5). DOI:10.1016/S0016-5085(10)60510-0 · 12.82 Impact Factor
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    ABSTRACT: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is an important quality indicator of the colon examination. In this paper, we present two new algorithms. The first algorithm determines whether an image shows the clearly seen appendiceal orifice. This algorithm uses our new local features based on geometric shape, illumination difference, and intensity changes along the norm direction (cross section) of an edge. The second algorithm determines whether the video is an appendix video (the video showing at least 3 s of the appendiceal orifice inspection). Such a video indicates good visualization of the appendiceal orifice. This algorithm utilizes frame intensity histograms to detect a near camera pause during the apendiceal orifice inspection. We tested our algorithms on 23 videos captured from two types of endoscopy procedures. The average sensitivity and specificity for the detection of appendiceal orifice images with the often seen crescent appendiceal orifice shape are 96.86% and 90.47%, respectively. The average accuracy for the detection of appendix videos is 91.30%.
    IEEE Transactions on Biomedical Engineering 04/2010; DOI:10.1109/TBME.2009.2034466 · 2.23 Impact Factor
  • Gastrointestinal Endoscopy 04/2010; 71(5). DOI:10.1016/j.gie.2010.03.427 · 4.90 Impact Factor
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    ABSTRACT: Colonoscopy is the preferred screening modality for prevention of colorectal cancer. Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant miss-rate for the detection of even large polyps and cancers. There was no automated measurement method to evaluate the quality of a colonoscopic procedure. To address this critical need, we have been investigating automated post-procedure quality measurement system. The limitation of post-processing quality measurement, however, is that quality measurements are available long after the procedure was done and the patient was released. We aim to achieve real-time analysis and feedback to aid the endoscopist towards optimal inspection to improve overall quality of colonoscopy during the procedure. Colonoscopy consists of two phases: an insertion phase and a withdrawal phase. One of the most essential tasks for the real-time quality measurement is to find a phase boundary between insertion and withdrawal phases in real time. In this paper, we will discuss how to find the phase boundary in real time. Our experimental results are promising.
    Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on; 10/2009
  • Gastrointestinal Endoscopy 04/2009; 69(5). DOI:10.1016/j.gie.2009.03.1093 · 4.90 Impact Factor
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    ABSTRACT: Advances in video technology are being incorporated into today's healthcare practices. Colonoscopy is regarded as one of the most important diagnostic tools for colorectal cancer. Indeed, colonoscopy has contributed to a decline in the number of colorectal-cancer-related deaths. Although colonoscopy has become the preferred screening modality for prevention of colorectal cancer, recent data suggest that there is a significant miss rate for the detection of large polyps and cancers, and methods to investigate why this occurs are needed. To address this problem, we present a new computer-based method that analyzes a digitized video file of a colonoscopic procedure and produces a number of metrics that likely reflect the quality of the procedure. The method consists of a set of novel image-processing algorithms designed to address new technical challenges due to uncommon characteristics of videos captured during colonoscopy. As these measurements can be obtained automatically, our method enables future quality control in large-scale day-to-day medical practice, which is currently not feasible. In addition, our method can be adapted to other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, and bronchoscopy. Last but not least, our method may be useful to assess progress during colonoscopy training.
    IEEE transactions on bio-medical engineering 11/2008; 56(9):2190-6. DOI:10.1109/TBME.2008.2006035 · 2.15 Impact Factor
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    ABSTRACT: This paper presents the design, implementation, and performance evaluation of a novel file uploading system. The system automatically uploads multimedia files to a centralized server given a client machine’s hard deadline—the time when a client machine will exhaust its available storage space due to on-going recording of media files. If existing files have not been uploaded and removed from the client machine’s hard disk by the deadlines, existing files may be overwritten or new files may not get created. Our uploading system was designed to provide a practical solution for emerging business needs. For instance, our system can be used in medical practice to gather videos generated from medical devices located in various procedure rooms for post-procedure analysis, and in law enforcement to collect video recordings from police cars during routine patrolling. Here we investigate two upload scheduling algorithms that determine which client machine should upload its file(s) first. We introduce two emergency control algorithms to handle situations when a client machine is about to exhaust its hard disk space. We evaluate the proposed algorithms via simulations and analysis. Our performance studies show that the upload scheduling algorithms and the emergency control algorithms have a significant impact on overall system performance.
    Multimedia Tools and Applications 04/2008; 38(1):51-74. DOI:10.1007/s11042-007-0149-0 · 1.06 Impact Factor
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    ABSTRACT: Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. The quality of bowel cleansing is generally assessed by the quantity of solid or liquid stool in the lumen. Despite a large body of published data on methods that could optimize cleansing, a substantial level of inadequate cleansing occurs in 10% to 75% of patients in randomized controlled trials. In this paper, a machine learning approach to the detection of stool in images of digitized colonoscopy video files is presented. The method involves the classification based on color features using a support vector machine (SVM) classifier. Our experiments show that the proposed stool image classification method is very accurate.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:3004-7. DOI:10.1109/IEMBS.2008.4649835
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    ABSTRACT: Reviewing medical videos for the presence of disease signs presents a unique problem to the conventional image classification tasks. The learning process based on imbalanced data set is heavily biased and tends to result in low sensitivity. In this article, we present a classification method for finding video frames that contain bleeding lesions. Our method re-balances the training samples by over-sampling the minority class and under-sampling the majority class. An SVM ensemble is then constructed using re-balanced data of three kinds of image features. Five sets of image frames were used in our experiments, each of which contains approximately 55,000 images and the ratio of minority and majority class is about 1:145. Our preliminary results demonstrated superior performance in sensitivity and comparative subjectivity with slight improvement.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:4780-3. DOI:10.1109/IEMBS.2008.4650282
  • [Show abstract] [Hide abstract]
    ABSTRACT: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is one of important quality indicators of examination of the colon. In this paper, we propose a new algorithm that detects appendix images-images showing the appendiceal orifice. We introduce new features based on geometric shape, saturation and intensity changes along the norm direction (cross-section) of an edge to discriminate appendix images. Our experimental results on real colonoscopic images show the average sensitivity and specificity of 88.12% and 94.25%, respectively.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:3000-3. DOI:10.1109/IEMBS.2008.4649834

Publication Stats

213 Citations
97.03 Total Impact Points

Institutions

  • 2007–2014
    • University of North Texas
      • Department of Computer Sciences & Engineering
      Denton, Texas, United States
  • 2004–2010
    • Iowa State University
      • Department of Computer Science
      Ames, IA, United States
  • 2006
    • Mayo Clinic - Rochester
      • Department of Gastroenterology and Hepatology
      Rochester, Minnesota, United States
  • 2002–2004
    • University of Texas at Arlington
      • Department of Computer Sciences & Engineering
      Arlington, TX, United States