Limits...
Automatic analysis of selected choroidal diseases in OCT images of the eye fundus.

Koprowski R, Teper S, Wróbel Z, Wylegala E - Biomed Eng Online (2013)

Bottom Line: For the cut decision tree the results were as follows: ACC1 = 0.76, ACC2 = 0.81, ACC3 = 0.68.The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging.In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul, Będzińska 39, Sosnowiec 41-200, Poland. koprow@us.edu.pl.

ABSTRACT

Introduction: This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessment of the treatment progress are a critical part of the eye fundus diagnosis.

Material and method: The study analysed about 1'000 OCT images acquired using SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The proposed algorithm for image analysis enabled to analyse the texture of the choroid portion located beneath the RPE (Retinal Pigment Epithelium) layer. The analysis was performed using the profiled algorithm based on morphological analysis and texture analysis and a classifier in the form of decision trees.

Results: The location of the centres of gravity of individual objects present in the image beneath the RPE layer proved to be important in the evaluation of different types of images. In addition, the value of the standard deviation and the number of objects in a scene were equally important. These features enabled classification of three different forms of the choroid that were related to retinal pathology: diabetic edema (the classification gave accuracy ACC1 = 0.73), ischemia of the inner retinal layers (ACC2 = 0.83) and scarring fibro vascular tissue (ACC3 = 0.69). For the cut decision tree the results were as follows: ACC1 = 0.76, ACC2 = 0.81, ACC3 = 0.68.

Conclusions: The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging. In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results. The image analysis method for texture analysis presented in the paper confirmed its usefulness in choroid imaging. Currently the application is further studied in the Clinical Department of Ophthalmology in the District Railway Hospital in Katowice, Medical University of Silesia, Poland.

Show MeSH

Related in: MedlinePlus

Image analysis of the sequence of images LKi. Subsequent results are shown for i∈(3, 5, 7, 9, 11). For each image LKi and thus for each i the values of the features from w(1) to w(20) are calculated. For example, one of the objects whose coordinates of the centre of gravity are calculated is shown on the top of the zoom – in this case, they are (176, 281). The features w(6) to w(16) are the mean value of gravity centre coordinates of all objects in the image LKi.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC3842656&req=5

Figure 3: Image analysis of the sequence of images LKi. Subsequent results are shown for i∈(3, 5, 7, 9, 11). For each image LKi and thus for each i the values of the features from w(1) to w(20) are calculated. For example, one of the objects whose coordinates of the centre of gravity are calculated is shown on the top of the zoom – in this case, they are (176, 281). The features w(6) to w(16) are the mean value of gravity centre coordinates of all objects in the image LKi.

Mentions: The values of pmn are in the range from 0 to 1, whereas the values of (1–pwe) should be non-negative in the range from 0 to 2. The values of (1–pwe) and (1 + pwd) for pwe = pwd = 0 are equal to 1, which means high intensity of conditional operations. For the other values of the thresholds pwe and pwd, for example for pwe = pwd = 1, there is a complete lack of effectiveness of erosion operations and significant effectiveness of dilation (the impact of selection of pwe and pwd values is presented later in this section). However, very often pmn(m,n) = const, irrespective of the location (pmn≠f(m,n)). Adopting pmn(m,n) = const is due to the nature of conditional operations, where in a general case a condition may not only be dependent on the mean values of sre and srd, but also on other values of the pixel saturation degree. These special properties of conditional dilation and erosion enable to obtain effective correction of the quality of the input images LBi. Sequential execution of conditional dilation and erosion (in this case, three times) allows to obtain corrected images LKi (Figure 3). The shape of the structural element SEi adopted in all of these relationships was as a circle of a pre-specified size because of the shape of the recognized objects. For each image LKi, characteristics were determined for each object. These features include:


Automatic analysis of selected choroidal diseases in OCT images of the eye fundus.

Koprowski R, Teper S, Wróbel Z, Wylegala E - Biomed Eng Online (2013)

Image analysis of the sequence of images LKi. Subsequent results are shown for i∈(3, 5, 7, 9, 11). For each image LKi and thus for each i the values of the features from w(1) to w(20) are calculated. For example, one of the objects whose coordinates of the centre of gravity are calculated is shown on the top of the zoom – in this case, they are (176, 281). The features w(6) to w(16) are the mean value of gravity centre coordinates of all objects in the image LKi.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3842656&req=5

Figure 3: Image analysis of the sequence of images LKi. Subsequent results are shown for i∈(3, 5, 7, 9, 11). For each image LKi and thus for each i the values of the features from w(1) to w(20) are calculated. For example, one of the objects whose coordinates of the centre of gravity are calculated is shown on the top of the zoom – in this case, they are (176, 281). The features w(6) to w(16) are the mean value of gravity centre coordinates of all objects in the image LKi.
Mentions: The values of pmn are in the range from 0 to 1, whereas the values of (1–pwe) should be non-negative in the range from 0 to 2. The values of (1–pwe) and (1 + pwd) for pwe = pwd = 0 are equal to 1, which means high intensity of conditional operations. For the other values of the thresholds pwe and pwd, for example for pwe = pwd = 1, there is a complete lack of effectiveness of erosion operations and significant effectiveness of dilation (the impact of selection of pwe and pwd values is presented later in this section). However, very often pmn(m,n) = const, irrespective of the location (pmn≠f(m,n)). Adopting pmn(m,n) = const is due to the nature of conditional operations, where in a general case a condition may not only be dependent on the mean values of sre and srd, but also on other values of the pixel saturation degree. These special properties of conditional dilation and erosion enable to obtain effective correction of the quality of the input images LBi. Sequential execution of conditional dilation and erosion (in this case, three times) allows to obtain corrected images LKi (Figure 3). The shape of the structural element SEi adopted in all of these relationships was as a circle of a pre-specified size because of the shape of the recognized objects. For each image LKi, characteristics were determined for each object. These features include:

Bottom Line: For the cut decision tree the results were as follows: ACC1 = 0.76, ACC2 = 0.81, ACC3 = 0.68.The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging.In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul, Będzińska 39, Sosnowiec 41-200, Poland. koprow@us.edu.pl.

ABSTRACT

Introduction: This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessment of the treatment progress are a critical part of the eye fundus diagnosis.

Material and method: The study analysed about 1'000 OCT images acquired using SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The proposed algorithm for image analysis enabled to analyse the texture of the choroid portion located beneath the RPE (Retinal Pigment Epithelium) layer. The analysis was performed using the profiled algorithm based on morphological analysis and texture analysis and a classifier in the form of decision trees.

Results: The location of the centres of gravity of individual objects present in the image beneath the RPE layer proved to be important in the evaluation of different types of images. In addition, the value of the standard deviation and the number of objects in a scene were equally important. These features enabled classification of three different forms of the choroid that were related to retinal pathology: diabetic edema (the classification gave accuracy ACC1 = 0.73), ischemia of the inner retinal layers (ACC2 = 0.83) and scarring fibro vascular tissue (ACC3 = 0.69). For the cut decision tree the results were as follows: ACC1 = 0.76, ACC2 = 0.81, ACC3 = 0.68.

Conclusions: The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging. In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results. The image analysis method for texture analysis presented in the paper confirmed its usefulness in choroid imaging. Currently the application is further studied in the Clinical Department of Ophthalmology in the District Railway Hospital in Katowice, Medical University of Silesia, Poland.

Show MeSH
Related in: MedlinePlus