Pair-Activity Analysis from Video Using Qualitative Trajectory Calculus

The automated analysis of interacting objects or people from video has many uses, including the recognition of activities, and identification of prototypical or unusual behaviors. Existing techniques generally use temporal sequences of quantifiable real-valued features, such as object position or orientation; however, more recently, qualitative representations have been proposed. In this paper we present a novel and robust qualitative method which can be used both for classification and clustering of pair-activities. We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between two objects, and encodes their interactions as a trajectory of QTC states. A key element is a general and robust means of determining the sequence similarity, which we term Normalized Weighted Sequence Alignment; we show that this is an effective metric for both recognition and clustering problems. We have evaluated our method across three different datasets, and shown that it out-performs state of the art quantitative methods, achieving an error rate of no more than 4.1% for recognition, and cluster purities higher than 90%. Our motivation originates from an interest in automated analysis of animal behaviors, and we present a comprehensive video dataset of fish behaviors (Gasterosteus aculeatus), collected from lab-based experiment

Optic nerve head and retinal abnormalities associated with congenital fibrosis of the extraocular muscles

Congenital fibrosis of the extraocular muscles (CFEOM) is a congenital cranial dysinnervation disorder caused by developmental abnormalities affecting cranial nerves/nuclei innervating the extraocular muscles. Autosomal dominant CFEOM arises from heterozygous missense mutations of KIF21A or TUBB3. Although spatiotemporal expression studies have shown KIF21A and TUBB3 expression in developing retinal ganglion cells, it is unclear whether dysinnervation extends beyond the oculomotor system. We aimed to investigate whether dysinnervation extends to the visual system by performing high-resolution optical coherence tomography (OCT) scans characterizing retinal ganglion cells within the optic nerve head and retina. Sixteen patients with CFEOM were screened for mutations in KIF21A, TUBB3, and TUBB2B. Six patients had apparent optic nerve hypoplasia. OCT showed neuro-retinal rim loss. Disc diameter, rim width, rim area, and peripapillary nerve fiber layer thickness were significantly reduced in CFEOM patients compared to controls (p < 0.005). Situs inversus of retinal vessels was seen in five patients. Our study provides evidence of structural optic nerve and retinal changes in CFEOM. We show for the first time that there are widespread retinal changes beyond the retinal ganglion cells in patients with CFEOM. This study shows that the phenotype in CFEOM extends beyond the motor nerves

The association between retinal vascular geometry changes and diabetic retinopathy and their role in prediction of progression: an exploratory study

Background: The study describes the relationship of retinal vascular geometry (RVG) to severity of diabetic retinopathy (DR), and its predictive role for subsequent development of proliferative diabetic retinopathy (PDR). Methods. The research project comprises of two stages. Firstly, a comparative study of diabetic patients with different grades of DR. (No DR: Minimal non-proliferative DR: Severe non-proliferative DR: PDR) (10:10: 12: 19). Analysed RVG features including vascular widths and branching angles were compared between patient cohorts. A preliminary statistical model for determination of the retinopathy grade of patients, using these features, is presented. Secondly, in a longitudinal predictive study, RVG features were analysed for diabetic patients with progressive DR over 7 years. RVG at baseline was examined to determine risk for subsequent PDR development. Results: In the comparative study, increased DR severity was associated with gradual vascular dilatation (p = 0.000), and widening of the bifurcating angle (p = 0.000) with increase in smaller-child-vessel branching angle (p = 0.027). Type 2 diabetes and increased diabetes duration were associated with increased vascular width (p = <0.05 In the predictive study, at baseline, reduced small-child vascular width (OR = 0.73 (95 CI 0.58-0.92)), was predictive of future progression to PDR. Conclusions: The study findings suggest that RVG alterations can act as novel markers indicative of progression of DR severity and establishment of PDR. RVG may also have a potential predictive role in determining the risk of future retinopathy progression. © 2014 Habib et al.; licensee BioMed Central Ltd

Spatial distribution of early red lesions is a risk factor for development of vision-threatening diabetic retinopathy

Aims/hypothesis Diabetic retinopathy is characterised by morphological lesions related to disturbances in retinal blood flow. It has previously been shown that the early development of retinal lesions temporal to the fovea may predict the development of treatment-requiring diabetic maculopathy. The aim of this study was to map accurately the area where lesions could predict progression to vision-threatening retinopathy. Methods The predictive value of the location of the earliest red lesions representing haemorrhages and/or microaneurysms was studied by comparing their occurrence in a group of individuals later developing vision-threatening diabetic retinopathy with that in a group matched with respect to diabetes type, age, sex and age of onset of diabetes mellitus who did not develop vision-threatening diabetic retinopathy during a similar observation period. Results The probability of progression to vision-threatening diabetic retinopathy was higher in a circular area temporal to the fovea, and the occurrence of the first lesions in this area was predictive of the development of vision-threatening diabetic retinopathy. The calculated peak value showed that the risk of progression was 39.5% higher than the average. There was no significant difference in the early distribution of lesions in participants later developing diabetic maculopathy or proliferative diabetic retinopathy. Conclusions/interpretation The location of early red lesions in diabetic retinopathy is predictive of whether or not individuals will later develop vision-threatening diabetic retinopathy. This evidence should be incorporated into risk models used to recommend control intervals in screening programmes for diabetic retinopathy

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available

Diabetic retinopathy: current and future methods for early screening from a retinal hemodynamic and geometric approach

Diabetic retinopathy (DR) is a major disease and is the number one cause of blindness in the UK. In England alone, 4200 new cases appear every year and 1280 lead to blindness. DR is a result of diabetes mellitus, which affects the retina of the eye and specifically the vessel structure. Elevated levels of glucose cause a malfunction in the cell structure, which affects the vessel wall and, in severe conditions, leads to their breakage. Much research has been carried out on detecting the different stages of DR but not enough versatile research has been carried out on the detection of early DR before the appearance of any lesions. In this review, the authors approach the topic from the functional side of the human eye and how hemodynamic factors that are impaired by diabetes affect the vascular structur

An algorithm combining two lesion-detection methods of retinal microaneurysms for the reduction of human workload

Purpose Reduction of workload in the detection of microaneurysms (MA) from retinal photographs is crucial for the diagnosis and screening of diabetic retinopathy. Automatic algorithms for the detection of retinal lesions can help reduce human intervention especially when the lesions are present in large numbers. Methods Two methods for lesion detection were combined in a single algorithm, one based on the analyses of the contrast between dark peak-points and surrounding circular regions, and a second one based on the correlation between the intensity values in the photographs and a MA-template. The two individual methods and the two methods combined were tested separately to compare their performance on retinal images from 26 high-risk patients. Results Both individual lesion-detection methods missed clustered MAs. With the exclusion of grouped lesions, the two methods combined showed higher sensitivity and precision than the contrast and template methods alone, identifying 22% and 13% more lesions respectively. Conclusions The combination of the two methods can provide repeatable detection of unclustered MAs in photographs from high-risk patients. Manual intervention is only required to select grouped MAs and to adjust the automatic results, considerably reducing human workload

A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. An obvious concern regarding the strength of these methodologies is the closeness of the result of a particular method to the gold standard. Unfortunately, the research community lacks benchmarks, resulting in increased subjective error, biased opinion and an uncertain progress. This paper introduces a manually-labeled, artery/vein categorized gold standard image database, as an extension of the most widely used image set DRIVE. The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty. A two-step validation phase consists of verification from the trained computer vision observers and a professional ophthalmologist, followed by a comparison with a gold standard set for the junction locations introduced in V4-Like filters. Our gold standard is in highly reliable form; offers research community for the result comparison and progress evaluation. © 2013 IEEE