Rashmi Turior - Academia.edu (original) (raw)

Papers by Rashmi Turior

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of severa... more The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of several ocular and systemic diseases. An automatic evaluation and quantification of tortuosity would help in the early detection of such pathologies. We applied two tortuosity evaluation approach based on continuous curvature to a dataset of 45 infant fundus images. Performance evaluation is done on classification accuracy of three classifiers-Naïve Bayesian classifier and k-nearest neighbor classifier, and K-means clustering algorithm, by comparing the estimated results against ground truth from expert ophthalmologists. Results show that different numerical methods provide different tortuosity values for same retinal vessels however have the potential to detect and evaluate abnormal retinal curves. The best classification accuracy of 87.3 % is achieved by the method 2 using K-nearest neighbor classifier.

ABSTRACT Abnormal retinal tortuosity is a significant diagnostic indicator for a number of retina... more ABSTRACT Abnormal retinal tortuosity is a significant diagnostic indicator for a number of retinal pathologies. We propose robust quantitative tortuosity metrics based on curvature calculated from improved chain code algorithm. In comparison to other published methods, the proposed metrics can estimate appropriate tortuosity for vessels with constant or changing curvature. We applied these metrics to a dataset of 200 infant retinal vessels and 100 simulated curves to demonstrate its validity as an indicator of changes in morphology. Tortuosity evaluation by the proposed measure is independent of the segmentation of the vessel tree. One of the proposed measure produced exactly the same rank-ordered list of vessel tortuosity values as obtained by averaging the tortuosity grading given by two ophthalmologists. Results show that our proposed metrics has potential to detect and evaluate abnormal retinal vascular structures in early diagnosis and prognosis of retinopathies.

Information and Communication Technology for Education, 2014

ABSTRACT Tortuosity classification is important for retinopathy of prematurity screening systems.... more ABSTRACT Tortuosity classification is important for retinopathy of prematurity screening systems. Timely and early detection can help reduce the incidence of blindness in premature infants. In this work, we implement and evaluate the performance of support vector machine classifier for tortuosity classification and compare it with three different classification methods. These consist of a Naïve Bayes classifier, a nearest neighbor classifier and K-means clustering technique. The classification accuracy is defined with respect to experts' graded ground truth blood vessels and the results are presented and comparatively analyzed.

Vascular dilation is a significant characteristic of Retinopathy of Prematurity (ROP). Timely pro... more Vascular dilation is a significant characteristic of Retinopathy of Prematurity (ROP). Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. In preterm retina the width of retinal veins and arteries are quantified by the proposed approach and the detected differences are displayed in a vessel map. Performance evaluation is done on average diameter of arteries to veins ratio, by comparing the estimated results against ground truth from expert ophthalmologists. Results are in accordance and show the appropriateness of our algorithm as applied to 20 infant images.

ABSTRACT Retinal vessel extraction is important for the de-tection of numerous eye diseases. It p... more ABSTRACT Retinal vessel extraction is important for the de-tection of numerous eye diseases. It plays an important role in automatic retinal disease screening systems. In this paper, vessel extraction algorithm based on combination of matched filter and bottom hat transform is proposed. First, green channel is extracted from original image. It is then applied to matched filter and bottom hat transform separately in order to enhance the contrast of vessels against background. Both enhanced images are extracted by thresholding process. Finally, two binary images are combined by aligning them together. Any pixel that appears in both binary images is considered as a vessel. The receiver operating characteristics (ROC), area under ROC and segmenta-tion accuracy is taken as the performance criteria. The method's performance is evaluated on two publicly available databases (DRIVE and STARE database) of manually labeled images. The results demonstrate that the proposed method outperforms other unsupervised methods in respect of maximum average accuracy (MAA). The proposed method results in the area under ROC and the accuracy of 0.8557, 0.9388 for DRIVE database 0.9019, 0.9405 for STARE database respectively.

The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011, 2011

Abstract Tortuosity is one of the first manifestations of many retinal diseases such as those due... more Abstract Tortuosity is one of the first manifestations of many retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vessel tortuosity would ...

International Conference on Electrical, Control and Computer Engineering 2011 (InECCE), 2011

Measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diag... more Measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. Screening of Retinopathy of Prematurity (ROP), a disease of eye that affects premature infants, for example, depends crucially on automatic tortuosity evaluation. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. In this paper, we propose the alternative method of automatic tortuosity measurement for retinal blood vessels that uses the curvature calculated from improved chain code algorithm taking the number of inflection point into account. The tortuosity calculated from the proposed method is independent of the segmentation of vessel tree. Our algorithm can automatically classify the image as tortuous or non-tortuous. The test results are verified against two expert ophthalmologists. For an optimal set of training parameters the prediction is as high as 100% on 18 images.

ScienceAsia, 2013

The clinical recognition of abnormal retinal tortuosity enables the diagnosis of many diseases. T... more The clinical recognition of abnormal retinal tortuosity enables the diagnosis of many diseases. Tortuosity is often interpreted as points of high curvature of the blood vessel along certain segments. Quantitative measures proposed so far depend on or are functions of the curvature of the vessel axis. In this paper, we propose a parallel algorithm to quantify retinal vessel tortuosity using a robust metric based on the curvature calculated from an improved chain code algorithm. We suggest that the tortuosity evaluation depends not only on the accuracy of curvature determination, but primarily on the precise determination of the region of support. The region of support, and hence the corresponding scale, was optimally selected from a quantitative experiment where it was varied from a vessel contour of two to ten pixels, before computing the curvature for each proposed metric. Scale factor optimization was based on the classification accuracy of the classifiers used, which was calculated by comparing the estimated results with ground truths from expert ophthalmologists for the integrated proposed index. We demonstrate the authenticity of the proposed metric as an indicator of changes in morphology using both simulated curves and actual vessels. The performance of each classifier is evaluated based on sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and positive likelihood ratio. Our method is effective at evaluating the range of clinically relevant patterns of abnormality such as those in retinopathy of prematurity. While all the proposed metrics are sensitive to curved or kinked vessels, the integrated proposed index achieves the best sensitivity and classification rate of 97.8% and 93.6%, respectively, on 45 infant retinal images.

Applied Mechanics and Materials, 2014

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease i... more Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods. Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists ground truths. Performance is evaluated based on measures as sensitivity, specificity, predictive values and accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy values obtained when considerin...

Applied Mechanics and Materials, 2012

Retinopathy of Prematurity (ROP) is a vital cause of vision loss in premature infants, but early ... more Retinopathy of Prematurity (ROP) is a vital cause of vision loss in premature infants, but early detection of its symptoms enables timely treatment and prevents blindness. Tortuosity is the major indicator of ROP that can potentially be automatically quantified. In this paper, which focuses on automatic tortuosity quantification and classification in images from infants at risk of ROP, we present a series of experiments on preprocessing, feature extraction, image feature selection and classification using nearest neighbor classifier. Fisher linear Discriminant analysis is used as a feature selection algorithm. We observe that the best feature set is a combination of two features: tortuosity as estimated based on combination of curvature of improved chain code and number of inflections and tortuosity as measured by inflection count metric. Accuracy, sensitivity and specificity are used as performance measures for the classifier. The results are validated against the judgments of expe...

Applied Mechanics and Materials, 2012

Almost all ocular and systemic diseases affect blood vessel attributes (tortuosity, length, width... more Almost all ocular and systemic diseases affect blood vessel attributes (tortuosity, length, width, and curvature). Quantitative measurements of these attributes could thus provide useful tool for diagnosing the severity of several diseases. However, it is still unclear how best to represent the attribute values of multiple vessels in a single image. Graphical user interface (GUI) is a promising step towards the development of a semi-automated computer assisted tool. The objective of this study is to develop a GUI for effective observation and robust retinal blood vessels analysis by ophthalmologists and to comprehend the distribution of vessels attributes. Blood vessels from 45 digital fundus images of infant retina are extracted, its centerline is delineated and tortuosity is analyzed from different putative and proposed techniques to provide reliable and comprehensive information for the retinal vasculature. K means clustering technique is used for classification analysis of diffe...

IEICE Transactions on Information and Systems, 2013

Rashmi TURIOR †a) , Student Member, Danu ONKAEW †b) , and Bunyarit UYYANONVARA †c) , Nonmembers S... more Rashmi TURIOR †a) , Student Member, Danu ONKAEW †b) , and Bunyarit UYYANONVARA †c) , Nonmembers SUMMARY 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.

Journal of Information Engineering and Applications, 2012

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of severa... more The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of several ocular and systemic diseases. An automatic evaluation and quantification of tortuosity would help in the early detection of such pathologies. We applied two tortuosity evaluation approach based on continuous curvature to a dataset of 45 infant fundus images. Performance evaluation is done on classification accuracy of three classifiers-Naive Bayesian classifier and k-nearest neighbor classifier, and K-means clustering algorithm, by comparing the estimated results against ground truth from expert ophthalmologists. Results show that different numerical methods provide different tortuosity values for same retinal vessels however have the potential to detect and evaluate abnormal retinal curves. The best classification accuracy of 87.3% is achieved by the method 2 using K-nearest neighbor classifier. Keywords: Retinal vessels, curvature, tortuosity

Computer Science and …, 2011

Retinal vessel extraction is important for the diagnosis of numerous eye diseases. It plays an im... more Retinal vessel extraction is important for the diagnosis of numerous eye diseases. It plays an important role in automatic retinal disease screening systems. This paper presents an efficient method for the automated analysis of retinal images. Fine anatomical features, ...

Applied Mechanics and Materials, Vol. 530-531, Jan 2014

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease i... more Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment
and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods.
Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists' ground truths.
Performance is evaluated based on measures as sensitivity, specificity, predictive values and
accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and
accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are
85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.

Science Asia Journal

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of many d... more The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of many diseases. Tortuosity is often interpreted as points of high curvature of the blood vessel along certain segments. Quantitative measures proposed so far depends on or are functions of the curvature of the vessel axis. In this paper we propose a parallel algorithm for the quantification of retinal vessel tortuosity, using robust metrics based on curvature calculated from improved chain code algorithm. We suggest that the tortuosity evaluation depends not only on the accuracy of curvature determination, but primarily on the precise determination of the region of support. The region of support, and hence the corresponding scale was optimally selected from a quantitative experiment where it was varied from a vessel contour of two to ten pixels, before computing the curvature for each proposed metrics. Scale factor optimization was based on Classifiers’ classification accuracy which was calculated by comparison of the estimated results and ground truths from expert ophthalmologists, for the integrated proposed index. We demonstrate the authenticity of the proposed metrics as an indicator of changes in morphology using both simulated curves and actual vessels. Classifiers’ performances are evaluated based on sensitivity, specificity, accuracy, positive predictive value, negative predictive value and positive likelihood ratio. Our methodology is effective at evaluating the range of clinically relevant patterns of abnormality such as those in Retinopathy of Prematurity. While all the proposed metrics are sensitive to curved or kinked vessels, the integrated proposed index achieves the best sensitivity and classification rate of 97.82% and 93.57% respectively on 45 infant retinal images.

IEICE Transactions Inf. & Systems, Feb 1, 2013

Automatic vessel tortuosity measure is crucial for many applications pertaining to retinal diseas... more Automatic vessel tortuosity measure is crucial for many applications pertaining 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 two other previously proposed 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 87.5% and 93.33% on 35 retinal images at segment level and image level respectively. The test results are verified against 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.

ECTICON 2011, Khon Khen, Thailand, May 10, 2011

Tortuosity is one of the first manifestations of many retinal diseases such as those due to reti... more Tortuosity is one of the first manifestations of many
retinal diseases such as those due to retinopathy of prematurity
(ROP), hypertension, stroke, diabetes and cardiovascular
diseases. An automatic evaluation and quantification of retinal
vessel tortuosity would help in the early detection of such
retinopathies and other systemic diseases. This paper proposes a
new approach based on principal component analysis (PCA), for
the evaluation of tortuosity in vessels extracted from digital
fundus images. One of the strength of the proposed algorithm is
that the index is independent of translation, rotation and scaling.
Measures are adopted such that the proposed approach matches
with the clinical concept of tortuosity. The algorithm is
compared with other available tortuosity measures. We have
demonstrated its validity as an indicator of changes in
morphology using simulated shapes. It is superior to other
putative indices, presented previously in literature.

Information and Communication Technology for Education, 2014

The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011, 2011

International Conference on Electrical, Control and Computer Engineering 2011 (InECCE), 2011

ScienceAsia, 2013

Applied Mechanics and Materials, 2014

Applied Mechanics and Materials, 2012

IEICE Transactions on Information and Systems, 2013

Journal of Information Engineering and Applications, 2012

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of severa... more The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of several ocular and systemic diseases. An automatic evaluation and quantification of tortuosity would help in the early detection of such pathologies. We applied two tortuosity evaluation approach based on continuous curvature to a dataset of 45 infant fundus images. Performance evaluation is done on classification accuracy of three classifiers-Naive Bayesian classifier and k-nearest neighbor classifier, and K-means clustering algorithm, by comparing the estimated results against ground truth from expert ophthalmologists. Results show that different numerical methods provide different tortuosity values for same retinal vessels however have the potential to detect and evaluate abnormal retinal curves. The best classification accuracy of 87.3% is achieved by the method 2 using K-nearest neighbor classifier. Keywords: Retinal vessels, curvature, tortuosity

Computer Science and …, 2011

Applied Mechanics and Materials, Vol. 530-531, Jan 2014

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease i... more Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment
and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods.
Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists' ground truths.
Performance is evaluated based on measures as sensitivity, specificity, predictive values and
accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and
accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are
85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.

Science Asia Journal

IEICE Transactions Inf. & Systems, Feb 1, 2013

ECTICON 2011, Khon Khen, Thailand, May 10, 2011