Qualification of the most statistically "sensitive" diffusion tensor imaging parameters for detection of spinal cord injury

Qualification of the most statistically "sensitive" diffusion parameters using Magnetic Resonance (MR) Diffusion Tensor Imaging (DTI) of the control and injured spinal cord of a rat in vivo and in vitro after the trauma is reported. Injury was induced in TH12/TH13 level by a controlled "weight-drop". In vitro experiments were performed in a home-built MR microscope, with a 6.4 T magnet, in vivo samples were measured in a 9.4 T/21 horizontal magnet The aim of this work was to find the most effective diffusion parameters which are useful in the statistically significant detection of spinal cord tissue damage. Apparent diffusion tensor (ADT) weighted data measured in vivo and in vitro on control and injured rat spinal cord (RSC) in the transverse planes and analysis of the diffusion anisotropy as a function of many parameters, which allows statisticall expose of the existence of the damage are reported

Counting of RBCs and WBCs in noisy normal blood smear microscopic images

This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells (RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better overall performance

Application of pattern recognition techniques for the analysis of thin blood smear images

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm

Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers

The isolated Wuchiapingian (Zechstein) Wielichowo Reef and its sedimentary and diagenetic evolution, SW Poland

The development of a relatively small and isolated part of the Wuchiapingian, Zechstein Wielichowo Reef was possible owing to a progressive subsidence and frequent sea level fluctuations. Three biofacies were distinguished within the studied object: (1) a shallow-water and highly energetic Acanthocladia biofacies, dominated by bryozoans and crinoids, with poorly preserved porosity, reduced mainly by calcite cementation and compaction; (2) the Horridonia biofacies comprising numerous brachiopods, preferring a moderate depth of water, with comparably poor porosity; and (3) the Fenestella/Kingopora biofacies rich in fossils of the highest variability, related to the deepest and calmest waters, occurring on the top of the profile and showing a significant effective porosity, reaching almost 13%. Among many diagenetic processes altering the reef, several lines of evidence suggest that it was the meteoric diagenesis to enhance its porosity the most extensively. Since no stromatolites are present, the final sea level decrease is interpreted to be rapid, hence creating conditions favourable for the meteoric dissolution. Some intraparticle porosity, however, seems to be of a depositional origin

Web–based framework for breast cancer classification

The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods

GLCM and GLRLM based texture features for computer-aided breast cancer diagnosis

This paper presents 15 texture features based on GLCM (Gray-Level Co-occurrence Matrix) and GLRLM (Gray-Level Run-Length Matrix) to be used in an automatic computer system for breast cancer diagnosis. The task of the system is to distinguish benign from malignant tumors based on analysis of fine needle biopsy microscopic images. The features were tested whether they provide important diagnostic information. For this purpose the authors used a set of 550 real case medical images obtained from 50 patients of the Regional Hospital in Zielona Góra. The nuclei were isolated from other objects in the images using a hybrid segmentation method based on adaptive thresholding and kmeans clustering. Described texture features were then extracted and used in the classification procedure. Classification was performed using KNN classifier. Obtained results reaching 90% show that presented features are important and may significantly improve computer-aided breast cancer detection based on FNB images

Evaluation of the properties of polymer composites with carbon nanotubes in the aspect of their abrasive wear

Purpose: Carbon nanotubes are used in composite materials due to the improvement of (including tribological) properties of composites, especially thermoplastic matrix composites. This demonstrates the potential of CNTs and the validity of research on determining the impact of this type of reinforcement on the composite materials under development. Design/methodology/approach: The article presents selected results of research on polymer composites made of C.E.S. R70 resin, C.E.S. H72 hardener with the addition of a physical friction modifier (CNTs) with a percentage by volume of 18.16% and 24.42%, respectively, which also acts as a reinforcement. The produced material was subjected to hardness measurements according to the Shore method and EDS analysis. The study of abrasive wear in reciprocating movement was carried out using the Taber Linear Abraser model 5750 tribotester and a precision weight. The surface topography of the composite material after tribological tests was determined using scanning electron microscopy (SEM). Some of the mentioned tests were carried out on samples made only of resin, used as the matrix of the tested polymer composite. Findings: Carbon nanotubes used in polymer matrix composites, including bisphenol A/F epoxy resin have an influence on the tribological properties of the material. The addition of carbon nanotubes contributed to a 24% increase in the Ra parameter relative to pure resin, to a level corresponding to rough grinding of steel. Research limitations/implications: The results of the tests indicate the need to continue research in order to optimize the composition of composites in terms of operating parameters of friction nodes in broadly understood aviation. Originality/value: The analysed literature did not find any studies on the impact of the addition of carbon nanotubes on epoxy resins based on bisphenol A/F. Due to the wide scope of application of such resins, the properties of such composite materials in which carbon nanotubes are the reinforcing phase have been investigated