Fractal analysis of CE CT lung tumours images

AIM The fractal dimension (FD) of a structure provides a measure of its complexity. This pilot study aims to determine FD values for lung cancers visualised on Computed Tomography (CT) and to assess the potential for tumour FD measurements to provide an index of tumour aggression. METHOD Pre-and post-contrast CT images of the thorax acquired from 15 patients with lung cancers of greater than 10mm were transformed to fractal dimension images using a box-counting algorithm at various scales. A region of interest (ROI) was determined covering tumour locations, which were more apparent on FD images as compared to images before processing. The average tumour FD (FDavg) was computed and compared with the intensity average before FD processing. FD values were correlated with 2 markers of tumour aggression: tumour stage and tumour uptake of fluorodeoxyglucose (FDG) as determined by Positron Emission Tomography. RESULTS For pre-contrast images, the tumour FDavg correlated with tumour stage (r = 0.537, p = 0.0387) and FDG uptake (r= 0.64, p< 0.001). FDavg decreased following contrast enhancement for most tumours. CONCLUSION Fractal analysis of CT images of lung tumours could potentially provide additional information about likely tumour aggression and so impact on clinical management decisions and choice of treatment

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure

Waterpipe tobacco smoking legislation and policy enactment: a global analysis

Objective (1) To review how current global tobacco control policies address regulation of waterpipe tobacco smoking (WTS). (2) To identify features associated with enactment and enforcement of WTS legislation. Data Sources (1) Legislations compiled by Tobacco Control Laws (www.tobaccocontrollaws.org). (2) Weekly news articles by ‘Google Alerts’ (www.google.com/alerts) from July 2013 to August 2014. Study Selection (1) Countries containing legislative reviews, written by legal experts, were included. Countries prohibiting tobacco sales were excluded. (2) News articles discussing aspects of the WHO FCTC were included. News articles related to electronic-waterpipe, crime, smuggling, opinion pieces or brief mentions of WTS were excluded. Data Abstraction (1) Two reviewers independently abstracted the definition of “tobacco product” and/or “smoking”. Four tobacco control domains (smokefree law, misleading descriptors, health warning labels and advertising/promotion/sponsorship) were assigned one of four categories based on the degree to which WTS had specific legislation. (2) Two investigators independently assigned at least one theme and associated subtheme to each news article. Data Synthesis (1) Reviewed legislations of 62 countries showed that most do not address WTS regulation but instead rely on generic tobacco/smoking definitions to cover all tobacco products. Where WTS was specifically addressed, no additional legislative guidance accounted for the unique way it is smoked, except for in one country specifying health warnings on waterpipe apparatuses (2) News articles mainly reported on noncompliance with public smoking bans, especially in India, Pakistan and the UK. Conclusions A regulatory framework evaluated for effectiveness and tailored for the specificities of WTS needs to be developed

Elaboration of Nanocomposites Based on Poly (Ethyl Methacrylate-co-Acrylonitrile) by in Situ Polymerization Using an Algerian Bentonite. Thermal Stability and Kinetic Study

This contribution focuses on the synthesis and characterization of nanocomposites based on poly (ethyl methacrylate-co-acrylonitrile) (PEMAN) and different loadings of an organically modified bentonite from Algeria prepared via in situ polymerization. TEM images and X-ray patterns revealed that depending on the loading of this clay, intercalated or partially exfoliated nanocomposites were obtained. These nanocomposites showed an increase in their glass transition temperature compared to the pure copolymer as investigated by Differential Scanning Calorimetry and improved thermal stability as evidenced by Thermogravimetric analysis and kinetics of their thermal degradation. Activation energies (Ea) of thermal decomposition of PEMAN and its nanocomposites were determined by Flynn–Wall–Ozawa and Kissinger-Akahira-Sunose methods. The changes in (Ea) value with the level of conversion suggest a significant improved thermal stability of the nanocomposites compared to the copolymer matrix. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3508

Combined statistical and model based texture features for improved image classification

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture measures and classifying the patterns using a naive Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based - Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) - were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as grey level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively