Automatic method for the dermatological diagnosis of selected hand skin features in hyperspectral imaging

Introduction: Hyperspectral imaging has been used in dermatology for many years. The enrichment of hyperspectral imaging with image analysis broadens considerably the possibility of reproducible, quantitative evaluation of, for example, melanin and haemoglobin at any location in the patient's skin. The dedicated image analysis method proposed by the authors enables to automatically perform this type of measurement. Material and method: As part of the study, an algorithm for the analysis of hyperspectral images of healthy human skin acquired with the use of the Specim camera was proposed. Images were collected from the dorsal side of the hand. The frequency λ of the data obtained ranged from 397 to 1030 nm. A total of 4'000 2D images were obtained for 5 hyperspectral images. The method proposed in the paper uses dedicated image analysis based on human anthropometric data, mathematical morphology, median filtration, normalization and others. The algorithm was implemented in Matlab and C programs and is used in practice. Results: The algorithm of image analysis and processing proposed by the authors enables segmentation of any region of the hand (fingers, wrist) in a reproducible manner. In addition, the method allows to quantify the frequency content in different regions of interest which are determined automatically. Owing to this, it is possible to perform analyses for melanin in the frequency range λE∈(450,600) nm and for haemoglobin in the range λH∈(397,500) nm extending into the ultraviolet for the type of camera used. In these ranges, there are 189 images for melanin and 126 images for haemoglobin. For six areas of the left and right sides of the little finger (digitus minimus manus), the mean values of melanin and haemoglobin content were 17% and 15% respectively compared to the pattern. Conclusions: The obtained results confirmed the usefulness of the proposed new method of image analysis and processing in dermatology of the hand as it enables reproducible, quantitative assessment of any fragment of this body part. Each image in a sequence was analysed in this way in no more than 100 ms using Intel Core i5 CPU M460 @2.5 GHz 4 GB RAM

Hermit Treutler's Jerusalem Balsam

This paper describes research about a historical bottle found in the Polish town of Skarszewy in 2004. Upon discovery, the find was labeled "In Nazareth Aechter Jerusalemer Balsam im goldnen Engel", sealed and ⅓ filled with liquid. The Jerusalem Balsam mentioned on the label was a popular medicament in Europe in the 18th century. From 1719 it was produced by Father Antonio Menzani da Cuna in the Franciscan Pharmacy at the convent of Saint Savior in Jerusalem. In the 19th century, the Balsam became extremely popular in Silesia thanks to the hermit Johannes Treutler from Mariańska Hill near Kłodzko. It's fame spread north to Prussia and south to Bohemia (Czechia). After the hermit's death, the license for production was obtained by the owner of the Mohren-Apotheke pharmacy, but he had to deal with unfair competition from other pharmacies counterfeiting the Balsam. An attempt was made to determine where the found bottle came from. In the course of the research, it was found that the medicine certainly does not come from authorized production sources, as evidenced by accurate label comparisons

Simple and non-invasive liver fibrosis stage prediction method

In this paper a simple and non-expensive indirect fibrosis stage prediction method is described. Presented method is non-invasive and is based on the results of the generic blood tests. The method is based on a statistical analysis of wide range of blood tests results supported with the experience of hepatologists

Classification techniques for non-invasive recognition of liver fibrosis stage

Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task