PCA-Based Retinal Vessel Tortuosity Quantification

Rashmi TURIOR; Danu ONKAEW; Bunyarit UYYANONVARA

doi:10.1587/transinf.E96.D.329

PCA-Based Retinal Vessel Tortuosity Quantification

Rashmi TURIOR, Danu ONKAEW, Bunyarit UYYANONVARA

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Automatic vessel tortuosity measures are crucial for many applications related to retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vascular tortuosity would help in the early detection of such retinopathies and other systemic diseases. In this paper, we propose a novel tortuosity index based on principal component analysis. The index is compared with three existant indices using simulated curves and real retinal images to demonstrate that it is a valid indicator of tortuosity. The proposed index satisfies all the tortuosity properties such as invariance to translation, rotation and scaling and also the modulation properties. It is capable of differentiating the tortuosity of structures that visually appear to be different in tortuosity and shapes. The proposed index can automatically classify the image as tortuous or non tortuous. For an optimal set of training parameters, the prediction accuracy is as high as 82.94% and 86.6% on 45 retinal images at segment level and image level, respectively. The test results are verified against the judgement of two expert Ophthalmologists. The proposed index is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, irrespective of the segmentation approach. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for both simulated curves and retinal images.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.2 pp.329-339

Publication Date: 2013/02/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.329

Type of Manuscript: PAPER

Category: Pattern Recognition

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Rashmi TURIOR, Danu ONKAEW, Bunyarit UYYANONVARA, "PCA-Based Retinal Vessel Tortuosity Quantification" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 2, pp. 329-339, February 2013, doi: 10.1587/transinf.E96.D.329.
Abstract: Automatic vessel tortuosity measures are crucial for many applications related to retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vascular tortuosity would help in the early detection of such retinopathies and other systemic diseases. In this paper, we propose a novel tortuosity index based on principal component analysis. The index is compared with three existant indices using simulated curves and real retinal images to demonstrate that it is a valid indicator of tortuosity. The proposed index satisfies all the tortuosity properties such as invariance to translation, rotation and scaling and also the modulation properties. It is capable of differentiating the tortuosity of structures that visually appear to be different in tortuosity and shapes. The proposed index can automatically classify the image as tortuous or non tortuous. For an optimal set of training parameters, the prediction accuracy is as high as 82.94% and 86.6% on 45 retinal images at segment level and image level, respectively. The test results are verified against the judgement of two expert Ophthalmologists. The proposed index is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, irrespective of the segmentation approach. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for both simulated curves and retinal images.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.329/_p

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@ARTICLE{e96-d_2_329,
author={Rashmi TURIOR, Danu ONKAEW, Bunyarit UYYANONVARA, },
journal={IEICE TRANSACTIONS on Information},
title={PCA-Based Retinal Vessel Tortuosity Quantification},
year={2013},
volume={E96-D},
number={2},
pages={329-339},
abstract={Automatic vessel tortuosity measures are crucial for many applications related to retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vascular tortuosity would help in the early detection of such retinopathies and other systemic diseases. In this paper, we propose a novel tortuosity index based on principal component analysis. The index is compared with three existant indices using simulated curves and real retinal images to demonstrate that it is a valid indicator of tortuosity. The proposed index satisfies all the tortuosity properties such as invariance to translation, rotation and scaling and also the modulation properties. It is capable of differentiating the tortuosity of structures that visually appear to be different in tortuosity and shapes. The proposed index can automatically classify the image as tortuous or non tortuous. For an optimal set of training parameters, the prediction accuracy is as high as 82.94% and 86.6% on 45 retinal images at segment level and image level, respectively. The test results are verified against the judgement of two expert Ophthalmologists. The proposed index is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, irrespective of the segmentation approach. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for both simulated curves and retinal images.},
keywords={},
doi={10.1587/transinf.E96.D.329},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - PCA-Based Retinal Vessel Tortuosity Quantification
T2 - IEICE TRANSACTIONS on Information
SP - 329
EP - 339
AU - Rashmi TURIOR
AU - Danu ONKAEW
AU - Bunyarit UYYANONVARA
PY - 2013
DO - 10.1587/transinf.E96.D.329
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2013
AB - Automatic vessel tortuosity measures are crucial for many applications related to retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vascular tortuosity would help in the early detection of such retinopathies and other systemic diseases. In this paper, we propose a novel tortuosity index based on principal component analysis. The index is compared with three existant indices using simulated curves and real retinal images to demonstrate that it is a valid indicator of tortuosity. The proposed index satisfies all the tortuosity properties such as invariance to translation, rotation and scaling and also the modulation properties. It is capable of differentiating the tortuosity of structures that visually appear to be different in tortuosity and shapes. The proposed index can automatically classify the image as tortuous or non tortuous. For an optimal set of training parameters, the prediction accuracy is as high as 82.94% and 86.6% on 45 retinal images at segment level and image level, respectively. The test results are verified against the judgement of two expert Ophthalmologists. The proposed index is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, irrespective of the segmentation approach. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for both simulated curves and retinal images.
ER -