Automated polyp detection in colon capsule endoscopy

Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy (CCE) 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. The geometrical analysis is based on a segmentation of an image with the help of a mid-pass filter. The features extracted by the segmentation procedure are classified according to an assumption that the polyps are characterized as protrusions that are mostly round in shape. Thus, we use a best fit ball radius as a decision parameter of a binary classifier. We present a statistical study of the performance of our approach on a data set containing over 18,900 frames from the endoscopic video sequences of five adult patients. The algorithm demonstrates a solid performance, achieving 47% sensitivity per frame and over 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 a human operator.Comment: 16 pages, 9 figures, 4 table

A Robust Image Hashing Algorithm Resistant Against Geometrical Attacks

This paper proposes a robust image hashing method which is robust against common image processing attacks and geometric distortion attacks. In order to resist against geometric attacks, the log-polar mapping (LPM) and contourlet transform are employed to obtain the low frequency sub-band image. Then the sub-band image is divided into some non-overlapping blocks, and low and middle frequency coefficients are selected from each block after discrete cosine transform. The singular value decomposition (SVD) is applied in each block to obtain the first digit of the maximum singular value. Finally, the features are scrambled and quantized as the safe hash bits. Experimental results show that the algorithm is not only resistant against common image processing attacks and geometric distortion attacks, but also discriminative to content changes

Molecular Interactions in Chiral Nematic Liquid Crystals and Enantiotopic Discrimination through the NMR Spectra of Achiral Molecules I: Rigid Solutes

We have developed a molecular theory for the enantiotopic discrimination in prochiral solutes dissolved in chiral nematic solvents by means of NMR spectroscopy. The leading rank tensor contributions to the proposed potential of mean torque include symmetric as well as antisymmetric terms with respect to spatial inversion, these lead to a consistent determination of all the prochiral solute symmetries for which enantiotopes are distinguishable by NMR and also to excellent quantitative agreement when tested against the available experimental data for the rigid solute acenaphthene and for the moderately flexible ethanol.Comment: 22 pages, 4 figure

Multidimensional scaling of D15 caps: Color-vision defects among tobacco smokers?

Tobacco smoke contains a range of toxins including carbon monoxide and cyanide. With specialized cells and high metabolic demands, the optic nerve and retina are vulnerable to toxic exposure. We examined the possible effects of smoking on color vision: specifically, whether smokers perceive a different pattern of suprathreshold color dissimilarities from nonsmokers. It is already known that smokers differ in threshold color discrimination, with elevated scores on the Roth 28-Hue Desaturated panel test. Groups of smokers and nonsmokers, matched for sex and age, followed a triadic procedure to compare dissimilarities among 32 pigmented stimuli (the caps of the saturated and desaturated versions of the D15 panel test). Multidimensional scaling was applied to quantify individual variations in the salience of the axes of color space. Despite the briefness, simplicity, and “low-tech” nature of the procedure, subtle but statistically significant differences did emerge: on average the smoking group were significantly less sensitive to red–green differences. This is consistent with some form of injury to the optic nerve

The ring compression test: Analysis of dimensions and canonical geometry

The compression ring test is universally accepted as a perfectly valid method by which determine simply and reliably the adhesion friction factor in a plastic deformation process. Its methodology is based on the application of geometric changes as both the reduction in thickness as the decrease in bore inner diameter in the strained ring itself. In this paper the performance of that test is the basis for establishing the coefficient of friction on a forging process so that, given this, its application to Upper Bound Theorem (UBT) by model Triangular Rigid Zones (TRZ), enable the establishment an intercomparison with empirical force, reaching a cuasivalidation of this Theorem in a certain range.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Parsimonious Mahalanobis Kernel for the Classification of High Dimensional Data

The classification of high dimensional data with kernel methods is considered in this article. Exploit- ing the emptiness property of high dimensional spaces, a kernel based on the Mahalanobis distance is proposed. The computation of the Mahalanobis distance requires the inversion of a covariance matrix. In high dimensional spaces, the estimated covariance matrix is ill-conditioned and its inversion is unstable or impossible. Using a parsimonious statistical model, namely the High Dimensional Discriminant Analysis model, the specific signal and noise subspaces are estimated for each considered class making the inverse of the class specific covariance matrix explicit and stable, leading to the definition of a parsimonious Mahalanobis kernel. A SVM based framework is used for selecting the hyperparameters of the parsimonious Mahalanobis kernel by optimizing the so-called radius-margin bound. Experimental results on three high dimensional data sets show that the proposed kernel is suitable for classifying high dimensional data, providing better classification accuracies than the conventional Gaussian kernel