A new approach to automated retinal vessel segmentation using multiscale analysis

Author name used in this publication: David ZhangRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Altair: Automatic Image Analyzer to Assess Retinal Vessel Caliber

The scope of this work is to develop a technological platform specialized in assessing retinal vessel caliber and describing the relationship of the results obtained to cardiovascular risk. Population studies conducted have found retinal vessel caliber to be related to the risk of hypertension, left ventricular hypertrophy, metabolic syndrome, stroke, and coronary artery disease. The vascular system in the human retina has a unique property: it is easily observed in its natural living state in the human retina by the use of a retinal camera. Retinal circulation is an area of active research by numerous groups, and there is general experimental agreement on the analysis of the patterns of the retinal blood vessels in the normal human retina. The development of automated tools designed to improve performance and decrease interobserver variability, therefore, appears necessary

Platform Image Processing Applied to the Study of Retinal Vessels

Recent studies have found retinal vessel caliber to be related to the risk of hypertension, left ventricular hypertrophy, metabolic syndrome, stroke and others coronary artery diseases. The vascular system in the human retina is easily perceived in its natural living state by the use of a retinal camera. Nowadays, there is general experimental agreement on the analysis of the patterns of the retinal blood vessels in the normal human retina. The development of automated tools designed to improve performance and decrease interobserver variability, therefore, appears necessary. This paper presents a study focused on developing a technological platform specialized in assessing retinal vessel caliber and describing the relationship of the results obtained to cardiovascular risk

Segmentation of Retinal Vasculature using Active Contour Models (Snakes)

Characteristic of retinal vasculature has been an important indicator for many diseases such as hypertension and diabetes. A digital image analysis system can assist medical experts to make accurate diagnosis in an efficient manner. This project presents the computer based approach to the automated segmentation of blood vessels in retinal images. The detection of the retinal vessel is achieved by performing image enhancement using CLAHE followed by Bottom-hat morphological transformation. Active contour model (snake) that based on level sets, techniques of curve evolution, and Mumford-Shah functional for segmentation is then used to segment out the detected retinal vessel and produce a complete retinal vasculature. A Graphic User Interface (GUI) has also been created to ease the user for the segmentation of the retinal vasculature. The algorithm is then tested with 20 test images from the DRIVE database. The results shows that the algorithm outperforms many other published methods and achieved an accuracy (ability to detect both vessel and non-vessel pixels) range of 0.92-0.94, a sensitivity (ability to detect vessel pixels) range of 0.91-0.95 and a specificity (ability to detect non-vessel pixels) range of0.78-0.85. I

Retinal imaging tool for assessment of the parapapillary atrophy and the optic disc

Ophthalmic diseases such as glaucoma are associated with progressive changes in the structure of the optic disc (OD) and parapapillary atrophy (PPA). These structural changes may therefore have relevance to other systemic diseases. The size and location of OD and PPA can be used as registration landmarks for monitoring changes in features of the fundus of the eye. Retinal vessel evaluation, for example, can be used as a biomarker for the effects of multiple systemic diseases, or co-morbidities. This thesis presents the first computer-aided measuring tool that detects and quantifies the progression of PPA automatically on a 2D retinal fundus image in the presence of image noise. An automated segmentation system is described that can detect features of the optic nerve. Three novel approaches are explored that extract the PPA and OD region approximately from a 2D fundus image. The OD region is segmented using (i) a combination of active contour and morphological operations, (ii) a modified Chan-Vese algorithm and (iii) a combination of edge detection and ellipse fitting methods. The PPA region is identified from the presence of bright pixels in the temporal zone of the OD, and segmented using a sequence of techniques, including a modified Chan-Vese approach, thresholding, scanning filter and multi-seed region growing methods. The work demonstrates for the first time how the OD and PPA regions can be identified and quantified from 2D fundus images using a standard fundus camera

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Quantitative Analysis of Conjunctival Vasculature

The purpose of this project was to investigate the utility of an automated means of quantifying the conjunctival bed. To simplify the task of any image processing system, it is important that the original image capture stage is optimised, this is an aspect that is often overlooked on the assumption that sophisticated computational techniques can be used to enhance the image. Hence, the concept of exposure density was used to achieve optimal images of bulbar conjunctival vessels using a digital camera (Kodak DCS 100 camera). A database of filters predicted a 6.5 times increase in vessel contrast when recording on the green channel of the charge coupled device (CCD) camera with a Wratten 99 (green) filter over the illumination optics, compared to recording without a filter. With knowledge of the optical transfer characteristics of the imaging system a vascular model was derived. The tubular model, corrected for optical distortions, was fitted to densitometric profiles across conjunctival vessels repetitively imaged under different optical configurations. Although vessel contrast did not increase by the predicted amount, a -30% increase in the amplitude was observed in comparison to images recorded on the green CCD alone. Hence, this became the method of choice when imaging vessels of the conjunctiva. Often automated methods of image segmentation are used without quantification of what is actually being measured, however, this is complicated when no accepted gold standard of measurement exists. Manual methods of determining widths using electronic callipers from projected digitally created photographs were used as the gold standard as they demonstrated a good intra session repeatability and range of measurement when 101 sample vessel widths were measured (95% confidence interval from +10.12 to -9.29pm, ranging from 14.4 to 140.0pm). To best agree with the automated measure of width, the algorithm was run at sigma (a) = 3, to give a 95% confidence interval of inter method repeatability of +9.41 to -8.48|im. However, it was acknowledged that this algorithm overestimates small vessel widths, and underestimates larger widths. The use of an automated approach of vessel recognition results in a vast amount of data concerning vessel axis and vessel edge locations. Five indices were derived to describe the vascular bed including mean vessel width, width variance, tortuosity, tortuosity variance and density, for vessels as a whole and for sub-groups of vessels classified on the basis of size. These indices were a novel way of describing the conjunctival vascular complex. The inter session repeatabilities of these indices were investigated on 31 normal patients and were acceptable in all cases. Also the diurnal variation in these indices on this population showed negligible changes. The angiopathic consequence of diabetes are well known in a variety of organs. However, these have never been adequately quantified in conjunctival vasculature. Seventeen Type I (TI) diabetics, 36 Type II (Til) diabetics, and 60 normals were analysed. Although several indices showed a difference between normals and diabetics for all vessels and sub-categories of vessels, by far the most remarkable was the dramatic change in density at a capillary level (vessels less that 25pm in diameter). A -57.12% (95% confidence interval from -71.96 to -36.76%, PcO.0001) reduction in capillary density was found in TI diabetics compared to normals and a reduction of - 17.5% (95% confidence interval from -41.02 to 16.64%) in Til diabetics compared to normals, however this was not statistically significant (P=0.273). A similar phenomenon was found in venular density (vessel 25 to less than 40|im in diameter). Hence, diabetes principally exerts its affect on the microvasculature of the conjunctival bed. In addition a statistically significant association between mean arterial pressure and vascular density was found, even though our sample did not contain anyone diagnosed with hypertension. A -13.82% (95% confidence interval from -23.71 to -2.65%, P=0.017) reduction in capillary density per lOmmHg rise in mean arterial pressure was established. Hence raised mean arterial pressure exerts an effect on smaller vessels of the conjunctiva