Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Exploring Australian intensive care physicians clinical judgement during donation after cardiac death : an exploratory qualitative study

BACKGROUND: Donation after Cardiac Death (DCD) is one possible solution to the world wide organ shortage. Intensive care physicians are central to DCD becoming successful since they are responsible for making the clinical judgements and decisions associated with DCD. Yet international evidence shows health care professionals have not embraced DCD and are often reluctant to consider it as an option for patients. PURPOSE: To explore intensive care physicians' clinical judgements when selecting a suitable DCD candidate. METHODS: Using interpretative exploratory methods six intensive care physicians were interviewed from three hospital sites in Australia. Following verbatim transcription, data was subjected to thematic analysis. FINDINGS: Three distinct themes emerged. Reducing harm and increasing benefit was a major focus of intensive care physicians during determination of DCD. There was an acceptance of DCD if there was clear evidence that donation was what the patient and family wanted. Characteristics of a defensible decision reflected the characteristics of sequencing, separation and isolation, timing, consensus and collaboration, trust and communication to ensure that judgements were robust and defensible. The final theme revealed the importance of minimising uncertainty and discomfort when predicting length of survival following withdrawal of life-sustaining treatment. CONCLUSION: DCD decisions are made within an environment of uncertainty due to the imprecision associated with predicting time of death. Lack of certainty contributed to the cautious and collaborative strategies used by intensive care physicians when dealing with patients, family members and colleagues around end-of-life decisions, initiation of withdrawal of life-sustaining treatment and the discussion about DCD. This study recommends that nationally consistent policies are urgently needed to increase the degree of certainty for intensive care staff concerning the DCD processes.

Application of a theoretical framework to foster a cardiac-diabetes self-management programme

AIM: This paper analyses and illustrates the application of Bandura's self-efficacy construct to an innovative self-management programme for patients with both type 2 diabetes and coronary heart disease. BACKGROUND: Using theory as a framework for any health intervention provides a solid and valid foundation for aspects of planning and delivering such an intervention; however, it is reported that many health behaviour intervention programmes are not based upon theory and are consequently limited in their applicability to different populations. The cardiac-diabetes self-management programme has been specifically developed for patients with dual conditions with the strategies for delivering the programme based upon Bandura's self-efficacy theory. This patient group is at greater risk of negative health outcomes than that with a single chronic condition and therefore requires appropriate intervention programmes with solid theoretical foundations that can address the complexity of care required. SOURCES OF EVIDENCE: The cardiac-diabetes self-management programme has been developed incorporating theory, evidence and practical strategies. DISCUSSION: This paper provides explicit knowledge of the theoretical basis and components of a cardiac-diabetes self-management programme. Such detail enhances the ability to replicate or adopt the intervention in similar or differing populations and/or cultural contexts as it provides in-depth understanding of each element within the intervention. CONCLUSION: Knowledge of the concepts alone is not sufficient to deliver a successful health programme. Supporting patients to master skills of self-care is essential in order for patients to successfully manage two complex, chronic illnesses. IMPLICATIONS FOR NURSING PRACTICE OR HEALTH POLICY: Valuable information has been provided to close the theory-practice gap for more consistent health outcomes, engaging with patients for promoting holistic care within organizational and cultural contexts.

An accuracy assessment of ASTER stereo images-derived digital elevation model by using rational polynomial coefficient model

The along-track stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor with 15 m resolution were used to generate Digital Elevation Model (DEM) on an area with low and near Mean Sea Level (MSL) elevation in Johor, Malaysia. The absolute DEM was generated by using the Rational Polynomial Coefficient (RPC) model which was run on ENVI 4.8 software. In order to generate the absolute DEM, 60 Ground Control Pointes (GCPs) with almost vertical accuracy less than 10 meter extracted from topographic map of the study area. The assessment was carried out on uncorrected and corrected DEM by utilizing dozens of Independent Check Points (ICPs). Consequently, the uncorrected DEM showed the RMSEz of ± 26.43 meter which was decreased to the RMSEz of ± 16.49 meter for the corrected DEM after post-processing. Overall, the corrected DEM of ASTER stereo images met the expectations.

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Tubule detection in testis images using boundary weighting and circular shortest path

In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually. © 2013 IEEE.

Calibration of treatment time intensity modulated EPID images for anatomical imaging

Intensity Modulated Radiotherapy (IMRT) is a well established technique for delivering highly conformal radiation dose distributions. The complexity of the delivery techniques and high dose gradients around the target volume make verification of the patient treatment crucial to the success of the treatment. Conventional treatment protocols involve imaging the patient prior to treatment, comparing the patient set-up to the planned set-up and then making any necessary shifts in the patient position to ensure target volume coverage. This paper presents a method for calibrating electronic portal imaging device (EPID) images acquired during IMRT delivery so that they can be used for verifying the patient set-up.

Standardising and assessing digital images for use in clinical trials : a practical, reproducible method that blinds the assessor to treatment allocation

With the increasing availability of high quality digital cameras that are easily operated by the non-professional photographer, the utility of using digital images to assess endpoints in clinical research of skin lesions has growing acceptance. However, rigorous protocols and description of experiences for digital image collection and assessment are not readily available, particularly for research conducted in remote settings. We describe the development and evaluation of a protocol for digital image collection by the non-professional photographer in a remote setting research trial, together with a novel methodology for assessment of clinical outcomes by an expert panel blinded to treatment allocation.

Assessment of the effects of CT dose in averaged x-ray CT images of a dose-sensitive polymer gel

The signal-to-noise ratio achievable in x-ray computed tomography (CT) images of polymer gels can be increased by averaging over multiple scans of each sample. However, repeated scanning delivers a small additional dose to the gel which may compromise the accuracy of the dose measurement. In this study, a NIPAM-based polymer gel was irradiated and then CT scanned 25 times, with the resulting data used to derive an averaged image and a "zero-scan" image of the gel. Comparison between these two results and the first scan of the gel showed that the averaged and zero-scan images provided better contrast, higher contrast-to- noise and higher signal-to-noise than the initial scan. The pixel values (Hounsfield units, HU) in the averaged image were not noticeably elevated, compared to the zero-scan result and the gradients used in the linear extrapolation of the zero-scan images were small and symmetrically distributed around zero. These results indicate that the averaged image was not artificially lightened by the small, additional dose delivered during CT scanning. This work demonstrates the broader usefulness of the zero-scan method as a means to verify the dosimetric accuracy of gel images derived from averaged x-ray CT data.