Diabetic autonomic neuropathy

Classification of factors influencing the use of infrared thermography in humans: A review

Automated localization and segmentation techniques for B-mode ultrasound images: A review.

Objectives. To collect normative baseline data and identify any significant differences between hand and foot thermographic distribution patterns in a healthy adult population. Design. A single-centre, randomized, prospective study. Methods. Thermographic data was acquired using a FLIR camera for the data acquisition of both plantar and dorsal aspects of the feet, volar aspects of the hands, and anterior aspects of the lower limbs under controlled climate conditions. Results. There is general symmetry in skin temperature between the same regions in contralateral limbs, in terms of both magnitude and pattern. There was also minimal intersubject temperature variation with a consistent temperature pattern in toes and fingers. The thumb is the warmest digit with the temperature falling gradually between the 2nd and the 5th fingers. The big toe and the 5th toe are the warmest digits with the 2nd to the 4th toes being cooler. Conclusion. Measurement of skin temperature of the limbs using a thermal camera is feasible and reproducible. Temperature patterns in fingers and toes are consistent with similar temperatures in contralateral limbs in healthy subjects. This study provides the basis for further research to assess the clinical usefulness of thermography in the diagnosis of vascular insufficiency.

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Thermographic patterns of the upper and lower limbs: baseline data.

Narrative review: Diabetic foot and infrared thermography

Hidden dangers revealed by misdiagnosed peripheral arterial disease using ABPI measurement

The segmentation of media-adventitia and lumen-intima boundaries of the Carotid Artery forms an essential part in assessing plaque morphology in Ultrasound Imaging. Manual methods are tedious and prone to variability and thus, developing automated segmentation algorithms is preferable. In this paper, we propose to use deep convolutional networks for automated segmentation of the media-adventitia boundary in transverse and longitudinal sections of carotid ultrasound images. Deep networks have recently been employed with good success on image segmentation tasks, and we thus propose their application on ultrasound data, using an encoder-decoder convolutional structure which allows the network to be trained end-to-end for pixel-wise classification. Concurrently, we evaluate the performance for various configurations, depths and filter sizes within the network. In addition, we further propose a novel fusion of envelope and phase congruency data as an input to the network, as the latter provides an intensity-invariant data source to the network. We show that this data fusion and the proposed network structure yields higher segmentation performance than the state-of-the-art techniques.

Automatic Carotid ultrasound segmentation using deep Convolutional Neural Networks and phase congruency maps

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.

/pdf/bimodal-automated-carotid-ultrasound-segmentation-using-30eliuwoy8.pdf

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks

Capsule Endoscopy is a technique designed to wirelessly image the small intestine within the gastrointestinal (GI) tract. Its main drawback is the vast amount of images it generates per patient, necessitating long screening sessions by the clinician. Previous studies have proposed to partially facilitate this process by automatically segmenting the GI tract into its constituent organs, thus identifying the region of interest. In this work, we propose to exploit the anatomical structure of the GI tract when carrying out dimensionality reduction on visual feature vectors that describe the capsule images. To this end, we suggest a novel adaptation of a technique called Locality Preserving Projections, and results show that this achieves an improved performance in organ classification and segmentation, at no additional computational or memory cost.

Carl Azzopardi

Papers

Thermographic patterns of the upper and lower limbs: baseline data.

Hidden dangers revealed by misdiagnosed peripheral arterial disease using ABPI measurement

Automatic Carotid ultrasound segmentation using deep Convolutional Neural Networks and phase congruency maps

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks

Exploiting gastrointestinal anatomy for organ classification in capsule endoscopy using locality preserving projections