[Show abstract][Hide abstract] ABSTRACT: Colonoscopy has contributed to a marked decline in
the number of colorectal cancer related deaths. However,
recent data suggest that there is a significant (4-12%) miss-rate
for the detection of even large polyps and cancers. To address
this, we have been investigating an ‘automated feedback system’
which informs the endoscopist of possible sub-optimal
inspection during colonoscopy. A fundamental step of this
system is to distinguish non-informative frames from
informative ones. Existing methods for this cannot classify
water/bubble frames as non-informative even though they do
not carry any useful visual information of the colon mucosa. In
this paper, we propose a novel texture feature based on
accumulation of pixel differences, which can detect water and
bubble frames with very high accuracy with significantly less
processing time. The experimental results show the proposed
feature can achieve more than 93% overall accuracy in almost
half of the processing time the existing methods take.
[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.
[Show abstract][Hide abstract] ABSTRACT: The effectiveness of colonoscopy depends on the quality of the inspection of the colon. There was no automated measurement method to evaluate the quality of the inspection. To address this, we have been investigating automated post-procedure quality measurement. The limitation of post-processing quality measurement is that quality measurements become available only long after the procedure was over and the patient was released. A better approach is to inform any suboptimal inspection immediately so that the endoscopist can improve the quality of the inspection in real-time during the procedure. Both post-processing and real-time quality measurements require a number of analysis tasks such as detecting a bite-block region as an indicator that a procedure is an upper endoscopy, not colonoscopy, detecting a blood region as an indicator for inflammation or bleeding, and detecting a stool region as an indicator of quality of the colon preparation. Color is the most distinguishable characteristic for differentiation among these object classes and normal pixels. In this paper, we propose a method to detect these object classes using color features. The main idea is to partition very large positive examples of these objects into a number of groups. Each group is called a “positive plane” and is modeled using a convex hull enclosing feature points of that particular group. Comparisons with traditional classifiers such as K-nearest neighbor (K-NN) and Support Vector Machines (SVM) prove the effectiveness of the proposed method in terms of accuracy and execution time that is critical in the targeted real-time quality measurement system.
[Show abstract][Hide abstract] 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).
No preview · Article · Oct 2013 · Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
No preview · Article · Aug 2013 · Computer methods and programs in biomedicine
[Show abstract][Hide abstract] 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.
No preview · Article · Oct 2012 · IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.