Pixel-based correspondence for facial images

A pixel-based correspondence method is presented for representation of facial images. The proposed method consists of two modules: face-image matching and face-image morphing. In the face-image matching module, the correspondence between two images are calculated for all pixel locations. A novel area-based matching method is proposed that makes use of the concept of the fractal dimension, and develops a non-parametric local transform as a basis for establishing correspondence between two face images. In the face-image morphing module, a mapping is performed for deformation of the source face image onto the target face image. This is done to map the pixels in the source face image to the location of their corresponding pixels in the target image.

Automatic detection of lung nodule from computer tomography images

Lung nodule refers to lung tissue abnormalities that may become cancerous. An automated system that detects nodules of common sizes within lung images is developed. It consists of acquisition, pre-processing, background removal, nodule detection, and false positives reduction. The system can assist expert radiologists in their decision making.

Automatic extraction of abnormal regions from lung images

A system that could automatically extract abnormal lung regions may assist expert radiologists in verifying lung tissue abnormalities. This paper presents an automated lung nodule detection system consisting of five components: acquisition, pre-processing, background removal, detection, and false positives reduction. The system employs a combination of an ensemble classification and clustering methods. The performance of the developed system is compared against some existing counterparts. Based 011 the experimental results, the proposed system achieved a sensitivity of 100% and a false-positives/slice of 0.67 for 30 tested CT images.

A neural network based human identification framework using ear images

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.

Student researchers in the middle : using visual images to make sense of inclusive education

Using ‘visual narrative’ theoretically and practically, this paper explores issues of inclusive education, during a period of curriculum reform and renewal in Australia. In Australia, the middle years of schooling, Years 5 to 9, are well researched and known as a period when students disengage with learning and participation in schooling. Research in the middle years affirms the importance of engaging with ‘student voice’. In this special edition, we are aiming to highlight how the use of visual imagery can be a rich source of understanding, illustrating students’ self-knowledge of schooling. Methodologically we refer to our research approach as ‘visual narrative’. Other writers in this edition use the term ‘photo voice’. For researchers it is important to highlight the differing orientations that ‘visual narrative’ and ‘photo voice’ signify. The terms are not mutually exclusive but highlight differing research possibilities and emphasis. Our argument, through the use of visual narrative produced by middle years’ students, is that visual texts open out some innovative possibilities for understanding inclusive education and supporting new relationships with our research community. Such approaches are not new; however, in a field such as special education that purports to support marginalised groups, liberatory research methods are under-represented. This paper aims to open out these discussions and provide a way forwards for teachers and researchers interested in breaking apart why it is that inclusive education remains a never-ending struggle.

Actual/ideal body images of high school girls and how it affects their self-esteem : implications for educational and clinical institutions

Given that adolescence is a critical period for the development of a positive self-concept and body image, it is imperative that educators have an understanding of adolescent students' body image and their dieting practises and behaviours. Negative body image is an issue that is rife within the adolescent student population in Australia and around the world, and is a factor that is commonly associated with and linked to disturbed eating behaviours and chronic eating disorders like Anorexia Nervosa (AN). Despite the intuitive appeal and importance of investigating adolescents' body image in terms of what adolescents would like to look like and what they think that they look like now, very little research has investigated these concepts. Furthermore, very little research has explored whether there is a difference between these two constructs how this may affect adolescents' self-esteem. This study therefore aimed to explore the actual and ideal body images of adolescent high school girls aged between 12 and 18 years in two samples of adolescents, one with eating disorders (n=76, clinical sample) and one without (n=823 high school sample). Additionally, this study investigates these constructs in relation to self-esteem levels. The results of this study have important implications for educators and clinicians within educational and clinical settings.

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

Children, visual images, and narratives

Book reviews of 
- Colomer, Teresa, Bettina Kümmerling-Meibauer, and Cecilia Silva-Diaz, eds. New Directions in Picturebook Research. New York: Routledge, 2010
- Edwards, Gail, and Judith Saltman. Picturing Canada: A History of Canadian Children’s Illustrated Books and Publishing. Toronto: U of Toronto P, 2010
- Harding, Jennifer, and Pat Pinsent, eds. What Do You See? International Perspectives on Children’s Book Illustration. Newcastle upon Tyne: Cambridge Scholars, 2008
- Lerner, Loren, ed. Depicting Canada’s Children. Waterloo: Wilfrid Laurier UP, 2009
- Pantaleo, Sylvia. Exploring Student Response to Contemporary Picturebooks. Toronto: U of Toronto P, 2009

Images of cross sections of some surgical suture thread

The data represents characterisation of future threads for orthopedic applications.

Image retrieval based on bag of images

Conventional relevance feedback schemes may not be suitable to all practical applications of content-based image retrieval (CBIR), since most ordinary users would like to complete their search in a single interaction, especially on the web search. In this paper, we explore a new approach to improve the retrieval performance based on a new concept, bag of images, rather than relevance feedback. We consider that image collection comprises of image bags instead of independent individual images. Each image bag includes some relevant images with the same perceptual meaning. A theoretical case study demonstrates that image retrieval can benefit from the new concept. A number of experimental results show that the CBIR scheme based on bag of images can improve the retrieval performance dramatically.