Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Direct visualisation of lipid bilayer cubic phases using Atomic Force Microscopy

Inverse bicontinuous cubic (Q(II)) phases are nanostructured materials formed by lipid self-assembly. We have successfully imaged thin films of hydrated Q(II) phases from two different systems using AFM. The images show periodic arrays of water channels with spacing and symmetry consistent with published SAXS data on the bulk materials.

Image restoration for a rectangular poor-pixels detector

This paper presents a unique two-stage image restoration framework especially for further application of a novel rectangular poor-pixels detector, which, with properties of miniature size, light weight and low power consumption, has great value in the micro vision system. To meet the demand of fast processing, only a few measured images shifted up to subpixel level are needed to join the fusion operation, fewer than those required in traditional approaches. By maximum likelihood estimation with a least squares method, a preliminary restored image is linearly interpolated. After noise removal via Canny operator based level set evolution, the final high-quality restored image is achieved. Experimental results demonstrate effectiveness of the proposed framework. It is a sensible step towards subsequent image understanding and object identification.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

English teachers in the digital age – a case study of policy and expert practice from England

This article is a case study of how English teachers in England have coped with the paradigm shift from print to digital literacy. It reviews a large scale national initiative that was intended to upskill all teachers, considers its weak impact and explores the author’s involvement in the evaluation of the project’s direct value to English teachers. It explores how this latter evaluation revealed how best practice in English using ICT was developing in a variable manner. It then reports on a recent small scale research project that investigated how very good teachers have adapted ICT successfully into their teaching. It focuses on how the English teachers studied in the project are developing a powerful new pedagogy situated in the life worlds of their students and suggests that this model may be of benefit to many teachers. The issues this article reports on have resonance in all English speaking countries. This article is also a personal story of the author’s close involvement with ICT and English over 20 years, and provides evidence for his conviction that digital technologies will eventually transform English teaching.

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

Continuous sky view factor maps from high resolution urban digital elevation models

This paper presents a new method to calculate sky view factors (SVFs) from high resolution urban digital elevation models using a shadow casting algorithm. By utilizing weighted annuli to derive SVF from hemispherical images, the distance light source positions can be predefined and uniformly spread over the whole hemisphere, whereas another method applies a random set of light source positions with a cosine-weighted distribution of sun altitude angles. The 2 methods have similar results based on a large number of SVF images. However, when comparing variations at pixel level between an image generated using the new method presented in this paper with the image from the random method, anisotropic patterns occur. The absolute mean difference between the 2 methods is 0.002 ranging up to 0.040. The maximum difference can be as much as 0.122. Since SVF is a geometrically derived parameter, the anisotropic errors created by the random method must be considered as significant.

Operational active fire mapping and burnt area identification applicable to Mexican Nature Protection Areas using MODIS and NOAA-AVHRR direct readout data

Since 1999, the National Commission for the Knowledge and Use of the Biodiversity (CONABIO) in Mexico has been developing and managing the “Operational program for the detection of hot-spots using remote sensing techniques”. This program uses images from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites and from the Advanced Very High Resolution Radiometer of the National Oceanic and Atmospheric Administration (NOAA-AVHRR), which are operationally received through the Direct Readout station (DR) at CONABIO. This allows the near-real time monitoring of fire events in Mexico and Central America. In addition to the detection of active fires, the location of hot spots are classified with respect to vegetation types, accessibility, and risk to Nature Protection Areas (NPA). Besides the fast detection of fires, further analysis is necessary due to the considerable effects of forest fires on biodiversity and human life. This fire impact assessment is crucial to support the needs of resource managers and policy makers for adequate fire recovery and restoration actions. CONABIO attempts to meet these requirements, providing post-fire assessment products as part of the management system in particular for satellite-based burnt area mapping. This paper provides an overview of the main components of the operational system and will present an outlook to future activities and system improvements, especially the development of a burnt area product. A special focus will also be placed on the fire occurrence within NPAs of Mexico

Understanding digital literacies: a practical introduction

Understanding Digital Literacies provides an accessible and timely introduction to new media literacies. It supplies readers with the theoretical and analytical tools with which to explore the linguistic and social impact of a host of new digital literacy practices. Each chapter in the volume covers a different topic, presenting an overview of the major concepts, issues, problems and debates surrounding the topic, while also encouraging students to reflect on and critically evaluate their own language and communication practices. Features include: coverage of a diverse range of digital media texts, tools and practices including blogging, hypertextual organisation, Facebook, Twitter, YouTube, Wikipedia, websites and games an extensive range of examples and case studies to illustrate each topic, such as how blogs have affected our thinking about communication, how the creation and sharing of digital images and video can bring about shifts in social roles, and how the design of multiplayer online games for children can promote different ideologies a variety of discussion questions and mini-ethnographic research projects involving exploration of various patterns of media production and communication between peers, for example in the context of Wikinomics and peer production, social networking and civic participation, and digital literacies at work end of chapter suggestions for further reading and links to key web and video resources a companion website providing supplementary material for each chapter, including summaries of key issues, additional web-based exercises, and links to further resources such as useful websites, articles, videos and blogs.

Collabowrighting the hyper(play)text: a postdramatic digital poetics

The aim of this research is to exhibit how literary playtexts can evoke multisensory trends prevalent in 21st century theatre. In order to do so, it explores a range of practical forms and theoretical contexts for creating participatory, site-specific and immersive theatre. With reference to literary theory, specifically to semiotics, reader-response theory, postmodernism and deconstruction, it attempts to revise dramatic theory established by Aristotle’s Poetics. Considering Gertrude Stein’s essay, Plays (1935), and relevant trends in theatre and performance, shaped by space, technology and the everchanging role of the audience member, a postdramatic poetics emerges from which to analyze the plays of Mac Wellman and Suzan-Lori Parks. Distinguishing the two textual lives of a play as the performance playtext and the literary playtext, it examines the conventions of the printed literary playtext, with reference to models of practice that radicalize the play form, including works by Mabou Mines, The Living Theatre and Fiona Templeton. The arguments of this practice-led Ph.D. developed out of direct engagement with the practice project, which explores the multisensory potential of written language when combined with hypermedia. The written thesis traces the development process of a new play, Rumi High, which is presented digitally as a ‘hyper(play)text,’ accessible through the Internet at www.RumiHigh.org. Here, ‘playwrighting’ practice is expanded spatially, collaboratively and textually. Plays are built, designed and crafted with many layers of meaning that explore both linguistic and graphic modes of poetic expression. The hyper(play)text of Rumi High establishes playwrighting practice as curatorial, where performance and literary playtexts are in a reciprocal relationship. This thesis argues that digital writing and reading spaces enable new approaches to expressing the many languages of performance, while expanding the collaborative network that produces the work. It questions how participatory forms of immersive and site-specific theatre can be presented as interactive literary playtexts, which enable the reader to have a multisensory experience. Through a reflection on process and an evaluation of the practice project, this thesis problematizes notions of authorship and text.

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Two- and three-dimensional shape fabric analysis by the intercept method in grey levels

The count intercept is a robust method for the numerical analysis of fabrics Launeau and Robin (1996). It counts the number of intersections between a set of parallel scan lines and a mineral phase, which must be identified on a digital image. However, the method is only sensitive to boundaries and therefore supposes the user has some knowledge about their significance. The aim of this paper is to show that a proper grey level detection of boundaries along scan lines is sufficient to calculate the two-dimensional anisotropy of grain or crystal distributions without any particular image processing. Populations of grains and crystals usually display elliptical anisotropies in rocks. When confirmed by the intercept analysis, a combination of a minimum of 3 mean length intercept roses, taken on 3 more or less perpendicular sections, allows the calculation of 3-dimensional ellipsoids and the determination of their standard deviation with direction and intensity in 3 dimensions as well. The feasibility of this quick method is attested by numerous examples on theoretical objects deformed by active and passive deformation, on BSE images of synthetic magma flow, on drawing or direct analysis of thin section pictures of sandstones and on digital images of granites directly taken and measured in the field. (C) 2010 Elsevier B.V. All rights reserved.

A compact and high-resolution version of a capacitively coupled contactless conductivity detector

An all-in-one version of a capacitively coupled contactless conductivity detector is introduced. The absence of moving parts (potentiometers and connectors) makes it compact (6.5 cm(3)) and robust. A local oscillator, working at 1.1 MHz, was optimized to use capillaries of id from 20 to 100 lam. Low noise circuitry and a high-resolution analog-to-digital converter (ADC) (21 bits effective) grant good sensitivities for capillaries and background electrolytes currently used in capillary electrophoresis. The fixed frequency and amplitude of the signal generator is a drawback that is compensated by the steady calibration curves for conductivity. Another advantage is the possibility of determining the inner diameter of a capillary by reading the ADC when air and subsequently water flow through the capillary. The difference of ADC reading may be converted into the inner diameter by a calibration curve. This feature is granted by the 21-bit ADC, which eliminates the necessity of baseline compensation by hardware. In a typical application, the limits of detection based on the 3 sigma criterion (without baseline filtering) were 0.6, 0.4, 0.3, 0.5, 0.6, and 0.8 mu mol/L for K(+), Ba(2+), Ca(2+), Na(+), Mg(2+), and Li(+), respectively, which is comparable to other high-quality implementations of a capacitively coupled contactless conductivity detector.