Fully automated breast boundary and pectoral muscle segmentation in mammograms

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in Computer Aided Diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100 mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively

Corner Detection on hexagonal pixel based images

Corner detection is used in many computer vision applications that require fast and efficient feature matching. In addition, hexagonal pixel based images have been recently investigated for image capture and processing due to their ability to represent curved structures that are common in real images better than traditional rectangular pixel based images. Therefore, we present an approach to corner detection on hexagonal images and demonstrate that accuracy is comparable to well-known existing corner detectors applied to rectangular pixel based images

Development of a technology adoption and usage prediction tool for assistive technology for people with dementia

This article is available open access through the publisher’s website at the link below. Copyright @ The Authors 2013.In the current work, data gleaned from an assistive technology (reminding technology), which has been evaluated with people with Dementia over a period of several years was retrospectively studied to extract the factors that contributed to successful adoption. The aim was to develop a prediction model with the capability of prospectively assessing whether the assistive technology would be suitable for persons with Dementia (and their carer), based on user characteristics, needs and perceptions. Such a prediction tool has the ability to empower a formal carer to assess, through a very limited amount of questions, whether the technology will be adopted and used.EPSR

Markovian Workload Characterization for QoS Prediction in the Cloud.

Resource allocation in the cloud is usually driven by performance predictions, such as estimates of the future incoming load to the servers or of the quality-of-service (QoS) offered by applications to end users. In this context, characterizing web workload fluctuations in an accurate way is fundamental to understand how to provision cloud resources under time-varying traffic intensities. In this paper, we investigate the Markovian Arrival Processes (MAP) and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud. MAPs are a special class of Markov models used as a compact description of the time-varying characteristics of workloads. In addition, MAPs can fit heavy-tail distributions, that are common in HTTP traffic, and can be easily integrated within analytical queueing models to efficiently predict system performance without simulating. By comparison with trace-driven simulation, we observe that existing techniques for MAP parameterization from HTTP log files often lead to inaccurate performance predictions. We then define a maximum likelihood method for fitting MAP parameters based on data commonly available in Apache log files, and a new technique to cope with batch arrivals, which are notoriously difficult to model accurately. Numerical experiments demonstrate the accuracy of our approach for performance prediction of web systems. © 2011 IEEE

Evaluation of a Technology Enabled Garment for Older Walkers

Walking is often cited as the best form of activity for persons over the age of 60. In this paper we outline the development and evaluation of a smart garment system that aims to monitor the wearer's wellbeing and activity regimes during walking activities. Functional requirements were ascertained using a combination of questionnaires and two workshops with a target cohort. The requirements were subsequently mapped onto current technologies as part of the technical design process. In this paper we outline the development and second round of evaluations of a prototype as part of a three-phase iterative development cycle. The evaluation was undertaken with 6 participants aged between 60 and 73 years of age. The results of the evaluation demonstrate the potential role that technology can play in the promotion of activity regimes for the older population

Computerised Skin Lesion Surface Analysis for Pigment Asymmetry Quantification

Malignant melanoma is the deadliest form of skin cancer and must be diagnosed and excised during its earliest stages. The development of computerised systems which accurately quantify features representative of this cancer aims to assist diagnosis and improve preoperative diagnostic accuracy. One clinical feature suggestive of malignancy is asymmetry, which considers lesion shape, colour distribution and texture. In this paper techniques for the detection of colour asymmetry are evaluated and a new method for visually displaying and quantifying colour asymmetry is proposed. Automatic induction methods and a neural network model are utilised to evaluate the diagnostic capability of our features and identify those of greatest relative importance. Results indicate that those features quantifying possible areas of regression are most indicative of colour asymmetry