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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.

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Related in: MedlinePlus

The method for obtaining tomographic images of the fundus. For a sample 2D tomographic image, the RPE layer (retina pigment epithelium) and the choroid layer CHO are highlighted. Image analysis applies to the proposed algorithm which analyses the choroid layer using new methods of texture analysis and mathematical morphology. In each case, a flat two-dimensional input image is analysed, whose resolution (and that of the OCT apparatus) does not affect the obtained results.
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Figure 1: The method for obtaining tomographic images of the fundus. For a sample 2D tomographic image, the RPE layer (retina pigment epithelium) and the choroid layer CHO are highlighted. Image analysis applies to the proposed algorithm which analyses the choroid layer using new methods of texture analysis and mathematical morphology. In each case, a flat two-dimensional input image is analysed, whose resolution (and that of the OCT apparatus) does not affect the obtained results.

Mentions: The input image LGRAY with a resolution M×N = 256×1024 pixels (M – number of rows, N – number of columns of the image) was subjected to median filtering using a mask h sized Mh×Nh = 3×3 pixels, thus obtaining the image LMED. The size of the filter mask h was chosen on the basis of medical evidence on the extent of artefacts found in this layer of the eye fundus OCT image and resolution of the image LGRAY. The images of successive stages of image pre-processing are shown in Figures 1 and 2. The image LMED is further analysed to detect the RPE layer yRPE ' (n) [3,15-17]. For this purpose, every n-th column of the image LMED is examined. The position of maximum brightness for each column is determined, i.e.:


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)

The method for obtaining tomographic images of the fundus. For a sample 2D tomographic image, the RPE layer (retina pigment epithelium) and the choroid layer CHO are highlighted. Image analysis applies to the proposed algorithm which analyses the choroid layer using new methods of texture analysis and mathematical morphology. In each case, a flat two-dimensional input image is analysed, whose resolution (and that of the OCT apparatus) does not affect the obtained results.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The method for obtaining tomographic images of the fundus. For a sample 2D tomographic image, the RPE layer (retina pigment epithelium) and the choroid layer CHO are highlighted. Image analysis applies to the proposed algorithm which analyses the choroid layer using new methods of texture analysis and mathematical morphology. In each case, a flat two-dimensional input image is analysed, whose resolution (and that of the OCT apparatus) does not affect the obtained results.
Mentions: The input image LGRAY with a resolution M×N = 256×1024 pixels (M – number of rows, N – number of columns of the image) was subjected to median filtering using a mask h sized Mh×Nh = 3×3 pixels, thus obtaining the image LMED. The size of the filter mask h was chosen on the basis of medical evidence on the extent of artefacts found in this layer of the eye fundus OCT image and resolution of the image LGRAY. The images of successive stages of image pre-processing are shown in Figures 1 and 2. The image LMED is further analysed to detect the RPE layer yRPE ' (n) [3,15-17]. For this purpose, every n-th column of the image LMED is examined. The position of maximum brightness for each column is determined, i.e.:

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