Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator.

Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.

5.6 μm quantum cascade lasers based on the Al0.78In0.22As/In0.69Ga0.31As active region composition with the measured pulsed room temperature wall plug efficiency of 28.3% are reported. Injection efficiency for the upper laser level of 75% was measured for the design by testing devices with variable cavity lengths. A threshold current density of 1.7 kA/cm2 and a slope efficiency of 4.9 W/A were measured for uncoated 3.15 mm × 9 μm lasers. Threshold current density and slope efficiency dependence on temperature in the range from 288 K to 348 K for the structure can be described by characteristic temperatures T0 ∼ 140 K and T1 ∼ 710 K, respectively.

Background: capsule endoscopy (CE) has revolutionized the study of small bowel. One major drawback of this technique is that we cannot interfere with image acquisition process. Therefore, the development of new software tools that could modify the images and increase both detection and diagnosis of small-bowel lesions would be very useful. The Flexible Spectral Imaging Color Enhancement (FICE) that allows for virtual chromoendoscopy is one of these software tools.Aims: to evaluate the reproducibility and diagnostic accuracy of the FICE system in CE.Methods: this prospective study involved 20 patients. First, four physicians interpreted 150 static FICE images and the overall agreement between them was determined using the Fleiss Kappa Test. Second, two experienced gastroenterologists, blinded to each other results, analyzed the complete 20 video streams. One interpreted conventional capsule videos and the other, the CE-FICE videos at setting 2. All findings were reported, regardless of their clinical value. Non-concordant findings between both interpretations were analyzed by a consensus panel of four gastroenterologists who reached a final result (positive or negative finding).Results: in the first arm of the study the overall concordance between the four gastroenterologists was substantial (0.650). In the second arm, the conventional mode identified 75 findings and the CE-FICE mode 95. The CE-FICE mode did not miss any lesions identified by the conventional mode and allowed the identification of a higher number of angiodysplasias (35 vs 32), and erosions (41 vs. 24).Conclusions: there is reproducibility for the interpretation of CE-FICE images between different observers experienced in conventional CE. The use of virtual chromoendoscopy in CE seems to increase its diagnostic accuracy by highlighting small bowel erosions and angiodysplasias that weren't identified by the conventional mode. INTRODUCTIONDigestive endoscopy has evolved from a pure diagnostic technique into a major interventional and therapeutic one. Even though, its value as a diagnostic tool has been kept and multiple developments have occurred to increase its diagnostic accuracy. The current technology of imageenhanced endoscopy is available to augment the detection, diagnosis and treatment of subtle lesions (1). Conventional white light endoscopy is associated with a disproportionate miss rate for subtle lesions (mainly flat). Therefore, the endoscopic manufacturers have developed several adjunct technologies working as point enhancement. One of these software tools is the Flexible Spectral Imaging Color Enhancement (FICE). It is easier and less time consuming than topical application of stains or pigments, since it is available with a simple button change to make multiple modifications of wavelength (2).The FICE system, manufactured by Fujinon Corporation (Saitama, Japan), is implemented based on Spectral Estimation Technology, which takes a real time endoscopic image from the video processor and arithmetically processes, estimates and produce...

Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.