A robust lesion boundary segmentation algorithm using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth

Segmentation quality evaluation for large scale mapping purposes in Flanders, Belgium

In Flanders the large scale reference database called GRB, takes care of the layout, exchange and management of large scale geographic information with respect to, amongst others, roads, buildings and parcels. As Flanders is extremely urbanized (average population density of about 450 inhabitants per square kilometer), the large scale maps need to be highly accurate. Currently, accuracies at the centimeter level are guaranteed due to topographic field measurements aided by standard photogrammetry based on analogue aerial photographs. In order to speed up the GRB production and to ensure large scale map products at the long term, it is essential to automate this labour-intensive, but highly accurate production process. Segmentation of very high resolution digital images could be an alternative approach for maintaining and updating the Flemish GRB as long as high accuracy segmentation results are obtained. Based on DMC images (8 cm ground resolution) and several reference buildings, a comprehensive sensitivity analysis is performed testing different segmentation parameter settings in order to gain insight into their impact on segmentation accuracy. The segmentation quality is evaluated using similarity measures focusing on aspects of presence, shape and positional accuracy where emphasis is placed on interpretability of the measures with respect to operational conditions put on the reference data. The end user should be able to read the measures and link this to the return-on-investment he will gain by using a given segmentation process on his data

Tracking Table Tennis Balls in Real Match Scenes for Umpiring Applications

Judging the legitimacy of table tennis services presents many challenges where technology can be judiciously applied to enhance decision-making. This paper presents a purpose-built system to automatically detect and track the ball during table-tennis services to enable precise judgment over their legitimacy in real-time. The system comprises a suite of algorithms which adaptively exploit spatial and temporal information from real match video sequences, which are generally characterised by high object motion, allied with object blurring and occlusion. Experimental results on a diverse set of table-tennis test sequences corroborate the system performance in facilitating consistently accurate and efficient decision-making over the validity of a service

Respiratory hospital admission risk near large composting facilities

AbstractBackgroundLarge-scale composting can release bioaerosols in elevated quantities, but there are few studies of health effects on nearby communities.MethodsA cross-sectional ecological small area design was used to examine risk of respiratory hospital admissions within 2500m of all 148 English large-scale composting facilities in 2008–10. Statistical analyses used a random intercept Poisson regression model at Census Output Area (COA) level (mean population 310). Models were adjusted for age, sex, deprivation and tobacco sales.ResultsAnalysing 34,963 respiratory hospital admissions in 4656 COAs within 250–2500m of a site, there were no significant trends using pre-defined distance bands of >250–750m, >750–1500m and >1500–2500m. Using a continuous measure of distance, there was a small non-statistically significant (p=0.054) association with total respiratory admissions corresponding to a 1.5% (95% CI: 0.0–2.9%) decrease in risk if moving from 251m to 501m. There were no significant associations for subgroups of respiratory infections, asthma or chronic obstructive pulmonary disease.ConclusionThis national study does not provide evidence for increased risks of respiratory hospital admissions in those living beyond 250m of an outdoor composting area perimeter. Further work using better measures of exposure and exploring associations with symptoms and disease prevalence, especially in vulnerable groups, is recommended to support regulatory approaches

A Sensitivity Study of L-Band Synthetic Aperture Radar Measurements to the Internal Variations and Evolving Nature of Oil Slicks

This thesis focuses on the use of multi-polarization synthetic aperture radar (SAR) for characterization of marine oil spills. In particular, the potential of detecting internal zones within oil slicks in SAR scenes are investigated by a direct within-slick segmentation scheme, along with a sensitivity study of SAR measurements to the evolving nature of oil slicks. A simple, k-means clustering algorithm, along with a Gaussian Mixture Model are separately applied, giving rise to a comparative study of the internal class structures obtained by both strategies. As no optical imagery is available for verification, the within-slick segmentations are evaluated with respect to the behavior of a set of selected polarimetric features, the prevailing wind conditions and weathering processes. In addition, a fake zone detection scheme is established to help determine if the class structures obtained potentially reflect actual internal variations within the slicks. Further, the evolving nature of oil slicks is studied based on the temporal development of a set of selected geometric region descriptors. Two data sets are available for the investigation presented in this thesis, both captured by a full-polarization L-band airborne SAR system with high spatial- and temporal resolution. The results obtained with respect to the zone detection scheme developed supports the hypothesis of the existence of detectable zones within oil spills in SAR scenes. Additionally, the method established for studying the evolving nature of oil slicks is found convenient for accessing the general behavior of the slicks, and simplifies interpretation

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy