Image registration and subtraction for the visualization of change in diabetic retinopathy screening

McRitchie, Ian N.; Hart, P. M.; Winder, R. J.

doi:10.1016/j.compmedimag.2006.01.002

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Over the past decade, the retinal image analysis has been widely used in medical community for diagnosing and monitoring the progression of diseases [1,2]. And retinal blood vessels are important structures in retinal images.…”

Section: Introductionmentioning

confidence: 99%

“…The information obtained from the examination of retinal blood vessels offers many useful parameters for the diagnosis or evaluation of ocular or systemic diseases. For example, the retinal blood vessel has shown some morphological changes such as diameter, length, branching angles or tortuosity for vascular or nonvascular pathology, such as hypertension, diabetes, cardiovascular diseases [3]. Blood vessels are also used as landmarks for registration of retinal images of a same patient gathered from different sources.…”

Section: Introductionmentioning

confidence: 99%

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A novel method for blood vessel detection from retinal images

Luo

2010

BioMed Eng OnLine

140

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BackgroundThe morphological changes of the retinal blood vessels in retinal images are important indicators for diseases like diabetes, hypertension and glaucoma. Thus the accurate segmentation of blood vessel is of diagnostic value.MethodsIn this paper, we present a novel method to segment retinal blood vessels to overcome the variations in contrast of large and thin vessels. This method uses adaptive local thresholding to produce a binary image then extract large connected components as large vessels. The residual fragments in the binary image including some thin vessel segments (or pixels), are classified by Support Vector Machine (SVM). The tracking growth is applied to the thin vessel segments to form the whole vascular network.ResultsThe proposed algorithm is tested on DRIVE database, and the average sensitivity is over 77% while the average accuracy reaches 93.2%.ConclusionsIn this paper, we distinguish large vessels by adaptive local thresholding for their good contrast. Then identify some thin vessel segments with bad contrast by SVM, which can be lengthened by tracking. This proposed method can avoid heavy computation and manual intervention.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel method for blood vessel detection from retinal images

Luo

2010

BioMed Eng OnLine

140

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“…Applications such as characterizing anatomical variability, monitoring changes due to disease or aging, and surgical planning have created the continuing need for research and development in registration [1–4]. The goal of medical image registration is to spatially align images so that they can be compared visually or quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

Ellingsen

Prince

2009

International Journal of Biomedical Imaging

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Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.

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