Automated drusen detection in retinal images using analytical modelling algorithms
© Mora et al; licensee BioMed Central Ltd. 2011
Received: 24 November 2010
Accepted: 12 July 2011
Published: 12 July 2011
Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD). They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually.
This article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI) by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area.
Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated.
The ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G); each expert compared to the other experts (E2E) and a standard Global Threshold method compared to the ground truth (T2G).
The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively.
The gradings produced by AD3RI obtained an agreement with the ground truth similar to the experts (with a higher reproducibility) and significantly better than the Threshold Method. Despite the higher sensitivity of the Threshold method, explained by its over segmentation bias, it has lower specificity and lower kappa coefficient. Therefore, it can be concluded that AD3RI accurately quantifies drusen, using a reproducible method with benefits for ARMD evaluation and follow-up.
The automatic analysis of retinal images can be influenced by several factors. Misalignment between patient eye and camera, contracted pupil or cataracts can produce images with non-uniform illumination patterns, making retina analysis more difficult. The correction of the image contrast is an important step to improve the automatic processing of the retinal images. Histogram equalization and specification have been used to normalize retinal images contrast [2–4]. However, they are not able to correct localized non-uniformities. Smith et al.  presented a method to correct the contrast on the macular region, which obtained a good normalization, but required user intervention to specify the macula location and did not correct other illumination distortions.
Several studies for drusen segmentation have been published in the last twenty-years. Local thresholds [6–11], global thresholds  or fuzzy logic thresholds  were some of the proposed solutions for drusen segmentation. However, threshold techniques are significantly tampered by noise, requiring a good noise removal method.
In this work we propose a novel methodology for automated drusen detection and quantification that includes:
The retinal images which were used to validate the proposed methodology were collected from two collaborating research centres. Twenty two film images were selected, digitalized and saved as bitmap with 1000 × 1000 24-bit colour pixels. Eight experts (four ophthalmologists and four trained technicians) marked digitally the existing drusen using the application, Manual Drusen Deposits Detection in Retinal Images (MD3RI) that was specifically developed for this purpose  and made available on the internet . This application allows computer assisted drawing of drusen contours, saving time and effort to the users and obtaining a very precise manual detection. In this study, the Wisconsin Grading System recommendations  were adopted. Following these recommendations, the inner-macula was defined as the region of interest (circular region of 3000 μm diameter around the Macula).
The proposed methodology defines all image processing steps to determine the area affected by drusen in order to establish a uniform analysis criterion. In the first processing step, the effects of non-uniform illumination are reduced and the contrast is normalized. The second processing step is the drusen detection, followed by the drusen modelling that detects and characterizes the drusen spots. On the fourth and last step, the affected area is quantified using the drusen model. The application Automatic Drusen Deposits Detection in Retinal Images (AD3RI) was developed for the validation of this methodology.
Similarly to several other works [8, 11, 19] only the green channel was selected for all the image processing. This channel usually offers a better drusen visibility by presenting a better contrast and less sensitivity to illumination abnormalities (when compared to the red and blue channels).
Non-uniform illumination correction
This equation contains two terms: the summation term (weighed by the smoothing factor p) that measures how close the spline is to the data, and the integral term (weighed by (1 - p)) that measures the spline smoothness using its second derivative.
The smoothing factor p, controls the balance between being an interpolating spline crossing all data points (with p = 1) and being a strictly smooth Spline (with p = 0). A too high p value will tend to produce, after the normalization, a flatter image, flattening also the drusen spots, which is a clearly unwanted side-effect. A too low p value will maintain drusen spots, but will not correct the illumination's non-uniformity.
In this fitting process, the large drusen areas influence negatively the illumination estimation, by being frequently evaluated as illumination. To overcome this, an iterative estimation process which masks drusen areas was implemented. It is based on the method proposed by Smith et al. , applied to the whole region of interest using two clusters.
estimation of the illumination pattern (1);
division of the original image by the estimated illumination pattern (2);
binarization of the image (using Otsu thresholding) to cluster the pixels into two classes: drusen and background (3); and
replacement of the pixels belonging to the drusen class by the estimated spline in the original image, creating an image without drusen's brighter areas (4).
This process is repeated 5 times (obtained empirically) progressively reducing the influence of higher intensity pixels on the next iteration. As result of this iterative process a corrected image with uniform illumination and without lost of contrast between the background and the drusen areas is obtained (Figure 2.c1 and 2.c2)).
Image contrast normalization
Depending on the original image contrast the non-uniform illumination correction can generate saturated or low contrasted images. This problem was corrected with the introduction of a contrast normalization procedure, achieved by normalizing the Root Mean Square contrast (RMSc)  to a predefined value. The RMS contrast calculation was based in the calculus of the contrast between retinal vessels and the background that was adopted as a reference contrast value. This calculus is applicable to any retinal image, as retinal vessels are always present.
The first stage of this labelling procedure is a pixel level analysis, following a top-left to bottom-right direction. It starts assigning a new label to each pixel and determining its gradient azimuth using a 3 × 3 Sobel operator (Figure 5.b), which is the direction to an ascending intensity. The following step, label propagation, propagates this label following the gradient path until an already marked or outside image boundaries pixel is found (Figures 5.c and 5.d). When the propagation process finishes on a different label, the two labels are tagged as equivalents, i.e., they are considered to belong to the same maximum (for example labels 2, 4, 6, 10 in Figures 5.d and 5.e).
The second stage of the labelling procedure is to apply the equivalences. Equivalent labels are grouped and replaced on the image by the smaller one of each group (Figure 5.f) producing a segmented image with as many labels as drusen spots.
When flat valleys or flat hills exist, not all gradient paths end on the same maximum pixel, resulting in an over-segmentation of the image. To solve this problem, a merging algorithm was introduced as the last stage of the labelling procedure.
After the merging, drusen are segmented and characterized by the coordinates and amplitude of their maximum intensity, which are the initial parameters for the modelling algorithm.
The Levenberg-Marquardt Least-Squares optimization algorithm  was used to fit the multiple elementary functions to the image (Figure 8.c) adjusting the functions parameters in order to minimize the mean square error between the model and the image. The algorithm was improved by including interval constraints in the amplitude and shape factor parameters, in order to guarantee the convergence of the solution and reduce computation time.
The modelling of the image by multiple functions, each containing eight adjustable parameters, is time consuming and can be non-convergent. To reduce complexity and improve convergence, an image sectioning method was implemented. Its goal was to create smaller images containing isolated or confluent drusen to be processed individually.
The sectioning process begins by applying a threshold to the normalized image (10% above the normalized background). The result is an image where drusen (isolated or confluent) are roughly identified and surrounded by background. The process is followed by a connected components object detection algorithm  to identify and label the drusen areas. Finally, these marked areas are copied from the original image to new smaller images containing just the identified drusen surrounded by background. These small images are then individually analysed by the modelling algorithm, requiring lower complexity.
Drusen Area Quantification
The contour of drusen spots and their area are calculated by thresholding the analytical model. The threshold value, that produces more accurate contours, was determined by comparing the false-positives and the false-negative pixels between the automated method and all the manually graded images. The threshold value is defined as a percentage of the background value used for the image normalization.
To validate and assess the accuracy of the automated method (AD3RI) it was compared to the gradings done by the experts and to a classical Global Threshold method. The Global Threshold method was applied to the normalized images and used an empirically found threshold value of 30% of the background value used for the image normalization.
For evaluation purposes it was assumed that a good performance of the AD3RI would be to obtain an overall score similar to the obtained by the experts. Experts were also evaluated among themselves, in order to produce an efficiency score for each of them.
AD3RI, Experts and Threshold gradings were assessed using both overall and local agreement indicators. Based on the total affected area, two overall indicators were used: the Coefficient of Variation (CV) and the Intraclass Correlation Coefficient (ICC). Local indicators sensitivity, specificity and kappa coefficient were based on a pixel-to-pixel analysis that determines false-positive and false-negative pixels.
Results and discussion
Automatic vs. manual measurements of Drusen per image
Summary of average indicators for automatic and manual measurements
When examining the accuracy on a pixel-to-pixel comparison, the AD3RI achieved an average sensitivity of 0.68 and an average specificity of 0.96, while the experts obtained 0.67 and 0.97. The slightly lower specificity obtained by the AD3RI was mainly due to the higher detection of drusen as consequence of a more detailed and systematic analysis. The kappa coefficient, analyzed accordingly to Landis and Koch guidelines , showed a moderate agreement for both AD3RI (k = 0.58) and experts average (k = 0.60).
The Threshold method showed a sensitivity of 0.74, higher than the average obtained by the experts as consequence of an over-detection of drusen. However this over-detection penalizes significantly its specificity (0.94) and its kappa coefficient (0.49).
From this statistical analysis it was concluded that the proposed algorithm follows the same criteria as the experts, although with a better accuracy and reproducibility.
The Global Threshold method showed a low agreement with the experts. Comparing Thresholding with AD3RI gradings, it can be observed that AD3RI, although with less detailed contours, has lower illumination dependency and provides more consistent drusen shape segmentation with higher reproducibility. The Threshold method is a simpler method, but shows an important tendency for drusen over-detection, producing a higher number of false-positives.
The analysis of the related work shows a large number of different methods and indicators for performance evaluation, limiting the comparison with our method. In the work of Rapantzikos et al.  their algorithm was tested in a set of twenty three images and compared to two experts analyses. For the specificity and sensitivity analysis the interception between the experts' gradings was used. This methodology decreased the probability of false-negatives, consequently rising sensitivity. These two indicators exceeded 0.96 in all cases, which can be considered excellent. However, it should be noted that the experts' interception is not a reliable method, since it eliminates variability, increasing sensitivity without compromising specificity. Smith et al.  evaluated their work with a dataset of twenty images examined by one expert, obtaining a sensitivity of 0.7 and a specificity of 0.8. Therefore, we can consider AD3RI more accurate namely because it was tested against a set of eight experts in order to achieve a more reliable ground-truth.
The development of methods to quantitatively measure drusen in a reproducible and accurate procedure will certainly improve the quality of the follow up of this disease and potentiate epidemiologic studies and clinical trials. These studies, that collect thousands of images throughout several years, must be graded using a reproducible method to allow comparison during all the study period. Currently, this is manually done by trained experts with a fastidious process, lacking accuracy and reproducibility.
This article presents a new method to quantitatively measure drusen and its' validation with 22 images graded by eight independent experts. The algorithm is based on the detection and modelling of drusen to automatically calculate the affected areas. It includes also an image pre-processing step to correct the non-uniform illumination commonly found on this type of images.
The illumination compensation algorithm is an important step to obtain a less parameterized methodology, since it is capable to create an image with normalized illumination and contrast to be used in all the remaining steps. The detection and modelling of drusen with Modified Gaussian functions demonstrated its capability to segment drusen keeping their typical shape, even on low contrast images.
It also provides an analytical model that allows the determination of drusen indicators such as number of spots, affected areas, confluence and average size.
Since there is no standard assessment technique to be applied in this type of studies, most of the published works use different performance indicators what makes comparison between studies inaccurate or even impossible. In our work, performance was assessed using several indicators allowing direct comparison with other studies. This comparison showed that the results produced by the AD3RI were similar or better than the others.
From the above, we considered that AD3RI demonstrated promising results. It compares positively with the panel of human experts and since is a determinist method; it is not dependent on factors such as attention or accuracy.
This work was partially financed by the Fundação para a Ciência e Tecnologia, through the POCTI and POSI Research Programs (project n° POCI/SAU-ESP/57592/2004).
The authors acknowledge the University of Aberdeen, Rudolfstiftung Hospital, Hospital Santa Maria and Universidade Nova de Lisboa for supplying the retinal images used in this work and for their valuable feedback while testing the software and grading the retinal images.
- Hageman GS, Luthert PJ, Victor Chong NH, Johnson LV, Anderson DH, Mullins RF: An integrated hypothesis that considers drusen as biomarkers of immune-mediated processes at the RPE-Bruch's membrane interface in aging and age-related macular degeneration. Prog Retin Eye Res 2001, 20: 705–732. 10.1016/S1350-9462(01)00010-6View ArticleGoogle Scholar
- Goatman KA, Whitwam AD, Manivannan A, Olson JA, Sharp PF: Colour normalisation of retinal images. Proceedings of Medical Image Understanding and Analysis; Sheffield 2003, 49–52.Google Scholar
- Salem NM, Nandi AK: Enhancement of Colour Fundus Images using Histogram Matching. In Proceedings of BioMED2005 - IASTED International Conference on Biomedical Engineering; February 16 - 18, 2005 Innsbruck, Austria Edited by: Adlassnig K-P, Bracale M. 2005.Google Scholar
- Gonzalez R, Woods R: Digital Image Processing. third edition. Prentice-Wall; 2007.Google Scholar
- Smith RT, Chan JK, Nagasaki T, Ahmad UF, Barbazetto I, Sparrow J, Figueroa M, Merriam J: Automated detection of macular drusen using geometric background leveling and threshold selection. Arch Ophthalmol 2005, 123: 200–206. 10.1001/archopht.123.2.200View ArticleGoogle Scholar
- Peli E, Lahav M: Drusen Measurement from Fundus Photographs Using Computer Image Analysis. Ophtalmology 1986, 93: 1575–1580.View ArticleGoogle Scholar
- Kirkpatrick JNP, Spencer T, Manivannan A, Sharp PF, Forrester JV: Quantitative image analysis of macular drusen from fundus photographs and scanning laser ophthalmoscope images. Eye (Royal College of Ophthalmologists) 1995, 9: 48–55.Google Scholar
- Rapantzikos K, Zervakis M, Balas K: Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med Image Anal 2003, 7: 95–108. 10.1016/S1361-8415(02)00093-2View ArticleGoogle Scholar
- Morgan WH, Cooper RL, Constable IJ, Eikelboom RH: Automated extraction and quantification of macular drusen from fundal photographs. Australian and New Zealand Journal of Ophthalmology 1994, 22: 7–12. 10.1111/j.1442-9071.1994.tb01688.xView ArticleGoogle Scholar
- Phillips RP, Spencer T, Ross PG, Sharp PF, Forrester JV: Quantification of diabetic maculopathy by digital imaging of the fundus. Eye 1991, 5(Pt 1):130–137.View ArticleGoogle Scholar
- Shin D, Javornik N, Berger J: Computer-assisted, interactive fundus image processing for macular drusen quantitation. Ophthalmology 1999, 106: 1119–1125. 10.1016/S0161-6420(99)90257-9View ArticleGoogle Scholar
- Smith RT, Nagasaki T, Sparrow JR, Barbazetto I, Klaver CC, Chan JK: A method of drusen measurement based on the geometry of fundus reflectance. Biomed Eng Online 2003, 2: 10. 10.1186/1475-925X-2-10View ArticleGoogle Scholar
- Thdibaoui A, Rajn A, Bunel P: A fuzzy logic approach to drusen detection in retinal angiographic images. Proceedings of 15th International Conference on Pattern Recognition; Barcelona, Spain 2000, 748–751.Google Scholar
- Mora A, Fonseca J, Vieira P: Drusen Deposits Modelling with Illumination Correction. In Proceedings of BioMED2005 - IASTED International Conference on Biomedical Engineering; February 16 - 18, 2005 Innsbruck, Austria Edited by: Adlassnig K-P, Bracale M. 2005.Google Scholar
- Mora A, Vieira P, Fonseca J: Modeling of Drusen Deposits Based on Retina Image Tridimensional Information. In Proceedings of Second International Conference on Computacional Intelligence in Medicine and Healthcare - CIMED-2005. Costa da Caparica, Portugal; 2005.Google Scholar
- Mora A, Vieira P, Fonseca J: MD3RI a Tool for Computer-Aided Drusens Contour Drawing. In Proceedings of Fourth IASTED International Conference on Biomedical Engineering - BIOMED2006; 15–17 February; Innsbruck, Austria. ACTA Press; 2006.Google Scholar
- MD3RI - Manual Drusen Deposits Detection on Retina Images [http://www.ca3-uninova.org/project_drusas]
- Klein R, Davis MD, Magli YL, Segal P, Klein BE, Hubbard L: The Wisconsin age-related maculopathy grading system. Ophthalmology 1991, 98: 1128–1134.View ArticleGoogle Scholar
- Soliz P, Wilson MP, Nemeth SC, Nguyen P: Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy. Proceedings of Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display; San Diego, CA, USA SPIE 2002, 159–170.View ArticleGoogle Scholar
- Culpin D: Calculation of cubic smoothing splines for equally spaced data. Numerische Mathematik 1986, 48: 627–638. 10.1007/BF01399686MATHMathSciNetView ArticleGoogle Scholar
- Smith RT, Chan JK, Nagasaki T, Sparrow JR, Barbazetto I: A method of drusen measurement based on reconstruction of fundus background reflectance. Br J Ophthalmol 2005, 89: 87–91. 10.1136/bjo.2004.042937View ArticleGoogle Scholar
- Peli E: Contrast in complex images. Journal of the Optical Society of America A 1990, 7: 2032–2040. 10.1364/JOSAA.7.002032View ArticleGoogle Scholar
- Marquardt DW: An algorithm for least-squares estimation of non-linear parameters. Journal of the Society for Industrial and Applied Mathematics 1963, 11: 431–441. 10.1137/0111030MATHMathSciNetView ArticleGoogle Scholar
- Landis J, Koch G: The measurement of observer agreement for categorical data. Biometrics 1977, 159–174.Google Scholar
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