Limits...
A method of drusen measurement based on the geometry of fundus reflectance.

Smith RT, Nagasaki T, Sparrow JR, Barbazetto I, Klaver CC, Chan JK - Biomed Eng Online (2003)

Bottom Line: The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield.The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield.This technique for macular image analysis has potential for use in clinical research.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Ophthalmology, Columbia University, New York, NY, USA. rts1@columbia.edu

ABSTRACT

Background: The hallmarks of age-related macular degeneration, the leading cause of blindness in the developed world, are the subretinal deposits known as drusen. Drusen identification and measurement play a key role in clinical studies of this disease. Current manual methods of drusen measurement are laborious and subjective. Our purpose was to expedite clinical research with an accurate, reliable digital method.

Methods: An interactive semi-automated procedure was developed to level the macular background reflectance for the purpose of morphometric analysis of drusen. 12 color fundus photographs of patients with age-related macular degeneration and drusen were analyzed. After digitizing the photographs, the underlying background pattern in the green channel was leveled by an algorithm based on the elliptically concentric geometry of the reflectance in the normal macula: the gray scale values of all structures within defined elliptical boundaries were raised sequentially until a uniform background was obtained. Segmentation of drusen and area measurements in the central and middle subfields (1000 microm and 3000 microm diameters) were performed by uniform thresholds. Two observers using this interactive semi-automated software measured each image digitally. The mean digital measurements were compared to independent stereo fundus gradings by two expert graders (stereo Grader 1 estimated the drusen percentage in each of the 24 regions as falling into one of four standard broad ranges; stereo Grader 2 estimated drusen percentages in 1% to 5% intervals).

Results: The mean digital area measurements had a median standard deviation of 1.9%. The mean digital area measurements agreed with stereo Grader 1 in 22/24 cases. The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield. The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield.

Conclusions: Semi-automated, supervised drusen measurements may be done reproducibly and accurately with adaptations of commercial software. This technique for macular image analysis has potential for use in clinical research.

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Iterative macular background leveling. Processing takes place in the green channel; gray scale is used here for better reproduction. (A) All pixels darker than a fixed threshold are marked in a pseudo-color, in this case green. Note that green areas consist of the darker points in the central background, and the retinal vessels. The operator selects the size of the magenta oval such that it is just large enough to include the darker points in the central background, ignoring the retinal vessels. All pixels within this oval (both background and drusen) are brightened by 2 color intensity scale units. (B) The image created in (A) is subsequently fed back into the same algorithm. Note that all pixels darker than the same threshold are again marked in green, but the central region of darkness becomes smaller and is enclosed by a smaller magenta oval. Visualization of the retinal vessels is unaffected. The new region within the oval is further brightened as before. (C) The image created in (B) is sent back through the same algorithm. Note that the central darker region is again reduced in size, since points at the edges that were just below the threshold in (B) have been brightened. The process is continued until the macular background is uniform.
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Figure 1: Iterative macular background leveling. Processing takes place in the green channel; gray scale is used here for better reproduction. (A) All pixels darker than a fixed threshold are marked in a pseudo-color, in this case green. Note that green areas consist of the darker points in the central background, and the retinal vessels. The operator selects the size of the magenta oval such that it is just large enough to include the darker points in the central background, ignoring the retinal vessels. All pixels within this oval (both background and drusen) are brightened by 2 color intensity scale units. (B) The image created in (A) is subsequently fed back into the same algorithm. Note that all pixels darker than the same threshold are again marked in green, but the central region of darkness becomes smaller and is enclosed by a smaller magenta oval. Visualization of the retinal vessels is unaffected. The new region within the oval is further brightened as before. (C) The image created in (B) is sent back through the same algorithm. Note that the central darker region is again reduced in size, since points at the edges that were just below the threshold in (B) have been brightened. The process is continued until the macular background is uniform.

Mentions: The interactive steps in the additive correction proceed as follows. The user is presented with a pseudo-color topographic map, which highlights those areas in the image whose background lies between the foveal minimum and the higher levels toward the arcades. In Figure 1, the green channel is presented in gray scale. The color green is the pseudo-color representing those pixels whose value is within a given range of the foveal minimum, i.e., the lowest background sources. The user then draws on a graphic tablet (Intuos, Wacom Corp., Vancouver, WA) an ellipse chosen to be just large enough to enclose the background of the given pseudo-color (Fig 1A, magenta ellipse surrounding the green areas of low background). Non-background dark sources (e.g., pigment, retinal vessels) are ignored. The gray scale value of each pixel in the selected region (background, drusen and all else) within the ellipse is then raised two units, and the process repeated (Fig 1B,1C). Since each step is deliberately chosen to be only a partial correction, several iterations are performed on the resulting image until there are no more background sources below this threshold. This partial correction per step was chosen as a reasonable way to force a smoother result, since each iteration uses a new set of ovals with boundary discontinuities limited to two units. In our experience, these are indiscernible in the final result. A higher range of background is then tested, and again the background areas beneath this minimum are step-wise increased. The process terminates when all background has been increased to the higher levels at the arcades, which are the highest macular background levels [26].


A method of drusen measurement based on the geometry of fundus reflectance.

Smith RT, Nagasaki T, Sparrow JR, Barbazetto I, Klaver CC, Chan JK - Biomed Eng Online (2003)

Iterative macular background leveling. Processing takes place in the green channel; gray scale is used here for better reproduction. (A) All pixels darker than a fixed threshold are marked in a pseudo-color, in this case green. Note that green areas consist of the darker points in the central background, and the retinal vessels. The operator selects the size of the magenta oval such that it is just large enough to include the darker points in the central background, ignoring the retinal vessels. All pixels within this oval (both background and drusen) are brightened by 2 color intensity scale units. (B) The image created in (A) is subsequently fed back into the same algorithm. Note that all pixels darker than the same threshold are again marked in green, but the central region of darkness becomes smaller and is enclosed by a smaller magenta oval. Visualization of the retinal vessels is unaffected. The new region within the oval is further brightened as before. (C) The image created in (B) is sent back through the same algorithm. Note that the central darker region is again reduced in size, since points at the edges that were just below the threshold in (B) have been brightened. The process is continued until the macular background is uniform.
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Figure 1: Iterative macular background leveling. Processing takes place in the green channel; gray scale is used here for better reproduction. (A) All pixels darker than a fixed threshold are marked in a pseudo-color, in this case green. Note that green areas consist of the darker points in the central background, and the retinal vessels. The operator selects the size of the magenta oval such that it is just large enough to include the darker points in the central background, ignoring the retinal vessels. All pixels within this oval (both background and drusen) are brightened by 2 color intensity scale units. (B) The image created in (A) is subsequently fed back into the same algorithm. Note that all pixels darker than the same threshold are again marked in green, but the central region of darkness becomes smaller and is enclosed by a smaller magenta oval. Visualization of the retinal vessels is unaffected. The new region within the oval is further brightened as before. (C) The image created in (B) is sent back through the same algorithm. Note that the central darker region is again reduced in size, since points at the edges that were just below the threshold in (B) have been brightened. The process is continued until the macular background is uniform.
Mentions: The interactive steps in the additive correction proceed as follows. The user is presented with a pseudo-color topographic map, which highlights those areas in the image whose background lies between the foveal minimum and the higher levels toward the arcades. In Figure 1, the green channel is presented in gray scale. The color green is the pseudo-color representing those pixels whose value is within a given range of the foveal minimum, i.e., the lowest background sources. The user then draws on a graphic tablet (Intuos, Wacom Corp., Vancouver, WA) an ellipse chosen to be just large enough to enclose the background of the given pseudo-color (Fig 1A, magenta ellipse surrounding the green areas of low background). Non-background dark sources (e.g., pigment, retinal vessels) are ignored. The gray scale value of each pixel in the selected region (background, drusen and all else) within the ellipse is then raised two units, and the process repeated (Fig 1B,1C). Since each step is deliberately chosen to be only a partial correction, several iterations are performed on the resulting image until there are no more background sources below this threshold. This partial correction per step was chosen as a reasonable way to force a smoother result, since each iteration uses a new set of ovals with boundary discontinuities limited to two units. In our experience, these are indiscernible in the final result. A higher range of background is then tested, and again the background areas beneath this minimum are step-wise increased. The process terminates when all background has been increased to the higher levels at the arcades, which are the highest macular background levels [26].

Bottom Line: The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield.The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield.This technique for macular image analysis has potential for use in clinical research.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Ophthalmology, Columbia University, New York, NY, USA. rts1@columbia.edu

ABSTRACT

Background: The hallmarks of age-related macular degeneration, the leading cause of blindness in the developed world, are the subretinal deposits known as drusen. Drusen identification and measurement play a key role in clinical studies of this disease. Current manual methods of drusen measurement are laborious and subjective. Our purpose was to expedite clinical research with an accurate, reliable digital method.

Methods: An interactive semi-automated procedure was developed to level the macular background reflectance for the purpose of morphometric analysis of drusen. 12 color fundus photographs of patients with age-related macular degeneration and drusen were analyzed. After digitizing the photographs, the underlying background pattern in the green channel was leveled by an algorithm based on the elliptically concentric geometry of the reflectance in the normal macula: the gray scale values of all structures within defined elliptical boundaries were raised sequentially until a uniform background was obtained. Segmentation of drusen and area measurements in the central and middle subfields (1000 microm and 3000 microm diameters) were performed by uniform thresholds. Two observers using this interactive semi-automated software measured each image digitally. The mean digital measurements were compared to independent stereo fundus gradings by two expert graders (stereo Grader 1 estimated the drusen percentage in each of the 24 regions as falling into one of four standard broad ranges; stereo Grader 2 estimated drusen percentages in 1% to 5% intervals).

Results: The mean digital area measurements had a median standard deviation of 1.9%. The mean digital area measurements agreed with stereo Grader 1 in 22/24 cases. The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield. The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield.

Conclusions: Semi-automated, supervised drusen measurements may be done reproducibly and accurately with adaptations of commercial software. This technique for macular image analysis has potential for use in clinical research.

Show MeSH
Related in: MedlinePlus