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.

Vidibus: a low-cost, modular bus system for real-time video processing

The advantages of standard bus systems have been appreciated for many years. The ability to connect only those modules required to perform a given task has both technical and commercial advantages over a system with a fixed architecture which cannot be easily expanded or updated. Although such bus standards have proliferated in the microprocessor field, a general purpose low-cost standard for digital video processing has yet to gain acceptance. The paper describes the likely requirements of such a system, and discusses three currently available commercial systems. A new bus specification known as Vidibus, developed to fulfil these requirements, is presented. Results from applications already implemented using this real-time bus system are also given.

The impact of novel labels on visual processing during infancy

The impact of novel labels on visual processing was investigated across two experiments with infants aged between 9 and 21 months. Infants viewed pairs of images across a series of preferential looking trials. On each trial, one image was novel, and the other image had previously been viewed by the infant. Some infants viewed images in silence; other infants viewed images accompanied by novel labels. The pattern of fixations both across and within trials revealed that infants in the labelling condition took longer to develop a novelty preference than infants in the silent condition. Our findings contrast with prior research by Robinson and Sloutsky (e.g., Robinson & Sloutsky, 2007a; Sloutsky & Robinson, 2008) who found that novel labels did not disrupt visual processing for infants aged over a year. Provided that overall task demands are sufficiently high, it appears that labels can disrupt visual processing for infants during the developmental period of establishing a lexicon. The results suggest that when infants are processing labels and objects, attentional resources are shared across modalities.

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.

Investigating subtypes of reward processing deficits as trait markers for depression

Background: Anhedonia, the loss of pleasure in usually enjoyable activities, is a central feature of major depressive disorder (MDD). The aim of the present study was to examine whether young people at a familial risk of depression display signs of anticipatory, motivational or consummatory anhedonia, which would indicate that these deficits may be trait markers for MDD. Methods: The study was completed by 22 participants with a family history of depression (FH+) and 21 controls (HC). Anticipatory anhedonia was assessed by asking participants to rate their anticipated liking of pleasant and unpleasant foods which they imagined tasting when cued with images of the foods. Motivational anhedonia was measured by requiring participants to perform key presses to obtain pleasant chocolate taste rewards or to avoid unpleasant apple tastes. Additionally, physical consummatory anhedonia was examined by instructing participants to rate the pleasantness of the acquired tastes. Moreover, social consummatory anhedonia was investigated by asking participants to make preference-based choices between neutral facial expressions, genuine smiles, and polite smiles. Results: It was found that the FH+ group’s anticipated liking of unpleasant foods was significantly lower than that of the control group. By contrast, no group differences in the pleasantness ratings of the actually experienced tastes or in the amount of performed key presses were observed. However, controls preferred genuine smiles over neutral expressions more often than they preferred polite smiles over neutral expressions, while this pattern was not seen in the FH+ group. Conclusion: These findings suggest that FH+ individuals demonstrate an altered anticipatory response to negative stimuli and show signs of social consummatory anhedonia, which may be trait markers for depression.

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.