title: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification

authors: Soares, J.V.B.

abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

text: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

year: 2006-09

title: How children find books for leisure reading: implications for the digital library

authors: Cunningham, Sally Jo

subjects: observational study

abstract: Finding a good book can be difficult, particularly for young readers. This paper adds to our understanding of how children select books for recreational reading by exploring the ‘native’ strategies (both successful and ineffective) that children employ in bookstores and libraries.

text: How children find books for leisure reading: implications for the digital library Finding a good book can be difficult, particularly for young readers. This paper adds to our understanding of how children select books for recreational reading by exploring the ‘native’ strategies (both successful and ineffective) that children employ in bookstores and libraries.

year: 2011

title: Respecting and recognising Indigenous rights when challenged by commercial activities

authors: Toki, Valmaine

abstract: The United Nations Declaration on the Rights of Indigenous Peoples was hailed as a triumph among Indigenous peoples, signalling a long-awaited recognition of their fundamental human rights. Despite this, many violations of these basic rights continue, particularly in relation to extractive industries and business activities. In response, a business reference guide seeks to inform industries of their responsibilities. This article examines the tenuous relationship between Indigenous rights, state responsibilities and business expectations.

text: Respecting and recognising Indigenous rights when challenged by commercial activities The United Nations Declaration on the Rights of Indigenous Peoples was hailed as a triumph among Indigenous peoples, signalling a long-awaited recognition of their fundamental human rights. Despite this, many violations of these basic rights continue, particularly in relation to extractive industries and business activities. In response, a business reference guide seeks to inform industries of their responsibilities. This article examines the tenuous relationship between Indigenous rights, state responsibilities and business expectations.

year: 2018