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.

We consider the subset E of $\RR^2$ of all points whose first and second components, respectively, coincide with the first and second eigenvalues of the Laplace operator $-\Delta$ with zero boundary conditions on domains of $\RR^N$ with prescribed measure. We show that the set E is closed in $\RR^2$.

Background and study aims Detection of polyps during colonoscopy is essential for screening colorectal cancer and computer-aided-diagnosis (CAD) could be helpful for this objective. The goal of this study was to assess the efficacy of CAD in detection of polyps in video colonoscopy by using three methods we have proposed and applied for diagnosis of polyps in wireless capsule colonoscopy. Patients and methods Forty-two patients were included in the study, each one bearing one polyp. A dataset was generated with a total of 1680 polyp instances and 1360 frames of normal mucosa. We used three methods, that are all binary classifiers, labelling a frame as either containing a polyp or not. Two of the methods (Methods 1 and 2) are threshold-based and address the problem of polyp detection (i. e. separation between normal mucosa frames and polyp frames) and the problem of polyp localization (i. e. the ability to locate the polyp in a frame). The third method (Method 3) belongs to the class of machine learning methods and only addresses the polyp detection problem. The mathematical techniques underlying these three methods rely on appropriate fusion of information about the shape, color and texture content of the objects presented in the medical images. Results Regarding polyp localization, the best method is Method 1 with a sensitivity of 71.8 %. Comparing the performance of the three methods in the detection of polyps, independently of the precision in the location of the lesions, Method 3 stands out, achieving a sensitivity of 99.7 %, an accuracy of 91.1 %, and a specificity of 84.9 %. Conclusion CAD, using the three studied methods, showed good accuracy in the detection of polyps with white light colonoscopy.

Falling, and the fear of falling, is a serious health problem among the elderly. It often results in physical and mental injuries that have the potential to severely reduce their mobility, independence and overall quality of life. Nevertheless, the consequences of a fall can be largely diminished by providing fast assistance. These facts have lead to the development of several automatic fall detection systems. Recently, many researches have focused particularly on smartphone-based applications. In this paper, we study the capacity of smartphone built-in sensors to differentiate fall events from activities of daily living. We explore, in particular, the information provided by the accelerometer, magnetometer and gyroscope sensors. A collection of features is analyzed and the efficiency of different sensor output combinations is tested using experimental data. Based on these results, a new, simple, and reliable algorithm for fall detection is proposed. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates 100 % for sensitivity and 93 % for specificity. Furthermore, evaluation conducted on a public dataset, for comparison with other existing smartphone-based fall detection algorithms, shows the high potential of the proposed method.