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Automatic cone photoreceptor segmentation using graph theory and dynamic programming.

Chiu SJ, Lokhnygina Y, Dubis AM, Dubra A, Carroll J, Izatt JA, Farsiu S - Biomed Opt Express (2013)

Bottom Line: This method is an extension of our previously described closed contour segmentation framework based on graph theory and dynamic programming (GTDP).We validated the performance of the proposed algorithm by comparing it to the state-of-the-art technique on a large data set consisting of over 200,000 cones and posted the results online.We found that the GTDP method achieved a higher detection rate, decreasing the cone miss rate by over a factor of five.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.

ABSTRACT
Geometrical analysis of the photoreceptor mosaic can reveal subclinical ocular pathologies. In this paper, we describe a fully automatic algorithm to identify and segment photoreceptors in adaptive optics ophthalmoscope images of the photoreceptor mosaic. This method is an extension of our previously described closed contour segmentation framework based on graph theory and dynamic programming (GTDP). We validated the performance of the proposed algorithm by comparing it to the state-of-the-art technique on a large data set consisting of over 200,000 cones and posted the results online. We found that the GTDP method achieved a higher detection rate, decreasing the cone miss rate by over a factor of five.

No MeSH data available.


Related in: MedlinePlus

Identification of cones missed by local maxima. (a) AOSLO image in log scale with missed cones shown inside the white boxes. (b) Cone photoreceptors segmented using local maxima initialization in black, and pilot estimates of missed cones found using deconvolution and local maxima are shown in white asterisks.
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g002: Identification of cones missed by local maxima. (a) AOSLO image in log scale with missed cones shown inside the white boxes. (b) Cone photoreceptors segmented using local maxima initialization in black, and pilot estimates of missed cones found using deconvolution and local maxima are shown in white asterisks.

Mentions: At this stage of the algorithm, the cones identified and segmented by the GTDP method (Fig. 2(b)Fig. 2


Automatic cone photoreceptor segmentation using graph theory and dynamic programming.

Chiu SJ, Lokhnygina Y, Dubis AM, Dubra A, Carroll J, Izatt JA, Farsiu S - Biomed Opt Express (2013)

Identification of cones missed by local maxima. (a) AOSLO image in log scale with missed cones shown inside the white boxes. (b) Cone photoreceptors segmented using local maxima initialization in black, and pilot estimates of missed cones found using deconvolution and local maxima are shown in white asterisks.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675871&req=5

g002: Identification of cones missed by local maxima. (a) AOSLO image in log scale with missed cones shown inside the white boxes. (b) Cone photoreceptors segmented using local maxima initialization in black, and pilot estimates of missed cones found using deconvolution and local maxima are shown in white asterisks.
Mentions: At this stage of the algorithm, the cones identified and segmented by the GTDP method (Fig. 2(b)Fig. 2

Bottom Line: This method is an extension of our previously described closed contour segmentation framework based on graph theory and dynamic programming (GTDP).We validated the performance of the proposed algorithm by comparing it to the state-of-the-art technique on a large data set consisting of over 200,000 cones and posted the results online.We found that the GTDP method achieved a higher detection rate, decreasing the cone miss rate by over a factor of five.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.

ABSTRACT
Geometrical analysis of the photoreceptor mosaic can reveal subclinical ocular pathologies. In this paper, we describe a fully automatic algorithm to identify and segment photoreceptors in adaptive optics ophthalmoscope images of the photoreceptor mosaic. This method is an extension of our previously described closed contour segmentation framework based on graph theory and dynamic programming (GTDP). We validated the performance of the proposed algorithm by comparing it to the state-of-the-art technique on a large data set consisting of over 200,000 cones and posted the results online. We found that the GTDP method achieved a higher detection rate, decreasing the cone miss rate by over a factor of five.

No MeSH data available.


Related in: MedlinePlus