A thrice daily meal-time BIAsp regimen is a suitable alternative to an intensified insulin regimen in people with inadequately controlled type 2 diabetes mellitus, and requires fewer daily injections than a basal-bolus therapy without compromising efficacy and safety.

A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.

Abstract. Malaria is an infectious disease which is mainly diagnosed by visual microscopical evaluation of Giemsa-stained thin blood films using a differential analysis of color features. This paper presents the evaluation of a color segmentation technique, based on standard supervised classification algorithms. The whole approach uses a general purpose classifier, which is parameterized and adapted to the problem of separating image pixels into three different classes: parasite, blood red cells and background. Assessment included not only four different supervised classification techniques -KNN, Naive Bayes, SVM and MLP -but different color spaces -RGB, normalized RGB, HSV and YCbCr-. Results show better performance for the KNN classifiers along with an improving feature characterization in the normalized RGB color space.

The paper reports on the histological effects of chronic implantation of self-sizing spiral cuff nerve electrodes on the cat sciatic nerve. The implantation period is about 4.4 months. Four different experimental conditions are evaluated: control, sham, bare cuff (cuffs without contacts and leads) and full cuff. The total number of axons in the nerves of the control group is compared with the three other groups. The surface occupied by collagen fibres in the nerve section, perineurium thickness, fibre diameter and myelin thickness are also measured. The average number of axons in the control nerves is found to be 16,416 (+/- 1,509) and does not differ significantly from the three other groups (p > 0.1). Collagen measurements show an extrafascicular epineurial fibrosis in the two implanted groups that is found to be significantly different (p < 0.05). No differences are encountered in the perineurium thickness analysis. Fibre diameter distributions show a regular bimodal pattern for all groups. Centrality (mean and Pm) and dispersion statistics (P25 and P75) extracted from fibre diameter distributions do not reveal significant differences. Myelin thickness distributions are also similar for all groups, as well as centrality and dispersion statistics. The present morphometrical results suggest that the effects produced by a chronic spiral cuff implant on this animal model are negligible.

Semantic annotation of microscopical field of views is one of the key problems in computer assistance of histopathological images. In this paper a new method for extracting patch descriptors is proposed and evaluated using a probabilistic latent semantic analysis (pLSA) classification model. The proposed approach is based on the analysis of the different dyes used to stain the histological sample. This analysis allows to find local regions that correspond to cells in the image, which are then described by the SIFT descriptors of the stain components. The proposed approach outperforms the conventional sampling and description strategies, proposed in the literature.