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Biomedical subjects

M Mirmehdi

Publications and source records attributed to M Mirmehdi.

2 recordsLinked to original sources

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.

Algorithms↗

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.

Color↗