Robust model-based vasculature detection in noisy biomedical images.

This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber's censored likelihood ratio test. The second is based on the use of a α -trimmed test stati...

Täydet tiedot

Bibliografiset tiedot

Julkaisussa:	IEEE Transactions on information technology in biomedicine 8, 3 (2004).
Päätekijä:	Mahadevan, V.
Aineistotyyppi:	Artikkeli
Kieli:	English
Aiheet:	spl alpha/-trimmed test statistic. Huber censored likelihood ratio test. Hypothesis testing. Intensity-texture profile. Noisy retinal video images. Nonlinear vessel enhancement filters. Phantom image construction. Retinal fundus images. Robust model selection algorithm. Robust model-based vasculature detection. Vasculature mathematical model. Vasculature segmentation.