A NOVEL AUTOMATIC METHOD FOR VESSEL TORTUOSITY EVALUATION
Year
: 2012
Abstract: Tortuosity evaluation of retinal or conjunctival vessels is one of the
significant steps in early treatment of diabetic retinopathy. Despite
the importance of this field, a few techniques have been proposed.
In this paper, we proposed a new automatic algorithm for
measuring vessel tortuosity based on Non Subsampled Contourlet
Transform (NSCT). Major vessels and their directional information
are extracted using NSCT in the first step. Then local vessel
curvature is computed using obtained NSCT information and entire
vessel network tortuosity is computed by combination of these
local curvature values. Accuracy of our algorithm is evaluated by
spearman correlation of our result and a set of images which are
ordered by an ophthalmologist in ascending manner of tortuosity.
We have shown that our algorithm achieves high accuracy in
evaluation of vessels network tortuosity beside less computational
time by reducing major steps of traditional tortuosity evaluation
algorithm.
significant steps in early treatment of diabetic retinopathy. Despite
the importance of this field, a few techniques have been proposed.
In this paper, we proposed a new automatic algorithm for
measuring vessel tortuosity based on Non Subsampled Contourlet
Transform (NSCT). Major vessels and their directional information
are extracted using NSCT in the first step. Then local vessel
curvature is computed using obtained NSCT information and entire
vessel network tortuosity is computed by combination of these
local curvature values. Accuracy of our algorithm is evaluated by
spearman correlation of our result and a set of images which are
ordered by an ophthalmologist in ascending manner of tortuosity.
We have shown that our algorithm achieves high accuracy in
evaluation of vessels network tortuosity beside less computational
time by reducing major steps of traditional tortuosity evaluation
algorithm.
Keyword(s): vessel tortuosity,retina,conjunctiva,NSCT
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A NOVEL AUTOMATIC METHOD FOR VESSEL TORTUOSITY EVALUATION
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| contributor author | فرنوش قدیری | en |
| contributor author | حمیدرضا پوررضا | en |
| contributor author | Touka Banaee | en |
| contributor author | Farnoosh Ghadiri | fa |
| contributor author | Hamid Reza Pourreza | fa |
| date accessioned | 2020-06-06T14:07:42Z | |
| date available | 2020-06-06T14:07:42Z | |
| date copyright | 4/11/2012 | |
| date issued | 2012 | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3383537?locale-attribute=en | |
| description abstract | Tortuosity evaluation of retinal or conjunctival vessels is one of the significant steps in early treatment of diabetic retinopathy. Despite the importance of this field, a few techniques have been proposed. In this paper, we proposed a new automatic algorithm for measuring vessel tortuosity based on Non Subsampled Contourlet Transform (NSCT). Major vessels and their directional information are extracted using NSCT in the first step. Then local vessel curvature is computed using obtained NSCT information and entire vessel network tortuosity is computed by combination of these local curvature values. Accuracy of our algorithm is evaluated by spearman correlation of our result and a set of images which are ordered by an ophthalmologist in ascending manner of tortuosity. We have shown that our algorithm achieves high accuracy in evaluation of vessels network tortuosity beside less computational time by reducing major steps of traditional tortuosity evaluation algorithm. | en |
| language | English | |
| title | A NOVEL AUTOMATIC METHOD FOR VESSEL TORTUOSITY EVALUATION | en |
| type | Conference Paper | |
| contenttype | External Fulltext | |
| subject keywords | vessel tortuosity | en |
| subject keywords | retina | en |
| subject keywords | conjunctiva | en |
| subject keywords | NSCT | en |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1030636.html | |
| conference title | 19th International Conference on Systems, Signals and Image Processing | en |
| conference location | وین | fa |
| identifier articleid | 1030636 |