Automated identification of diabetic retinal exudates in digital colour images.

AIM: To identify retinal exudates automatically from colour retinal images. METHODS: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. RESULTS: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. CONCLUSIONS: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.

Musculoskeletal motion flow fields using hierarchical variable-sized block matching in ultrasonographic video sequences.

We examine tissue deformations using non-invasive dynamic musculoskeletal ultrasonograhy, and quantify its performance on controlled in vitro gold standard (groundtruth) sequences followed by clinical in vivo data. The proposed approach employs a two-dimensional variable-sized block matching algorithm with a hierarchical full search. We extend this process by refining displacements to sub-pixel accuracy. We show by application that this technique yields quantitatively reliable results.

Feedback control strategies for object recognition.

We present a paradigm for feedback strategies that find instances of a generic class of objects by improving on established single-pass hypothesis generation and verification approaches. We improve upon the mechanisms of the traditional or classical image processing systems by introducing control strategies at low, intermediate, and high levels of analysis. We produce optimal sets of low-level features to reduce the number of hypotheses generated. The feedback further enables updated sets of features to be extracted so that the target object may be located even in very, noisy data. The use of an interest operator in the feedback directs the search through the hypotheses in an optimal manner, so minimizing the amount of feedback to false alarms. Furthermore, we aim to obtain detailed information about a complex object and not just its location. Thus, following top-down recognition of the object our feedback control directs the search for missing information. The system can extract complex objects in a scale and rotation independent manner where the objects may be partially occluded. The method is illustrated using box shaped objects and noisy IR images of a number of bridges.