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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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The graph of occurrence frequency of individual features w(1) to w(20) for the complete decision tree and the cut one for the first top 1′000 results. The graph shows that the highest occurrence concerns the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. The occurrence frequency of the features corresponds with the results shown in Tables 2 and 3.
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Figure 8: The graph of occurrence frequency of individual features w(1) to w(20) for the complete decision tree and the cut one for the first top 1′000 results. The graph shows that the highest occurrence concerns the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. The occurrence frequency of the features corresponds with the results shown in Tables 2 and 3.

Mentions: In the classification of groups Z1, Z2, Z3 the features from w(9) to w(12) as well as from w(18) to w(20) did not occur. This means that their influence can be neglected for the best classifications (top 3 results). These features (from w(9) to w(12) as well as from w(18) to w(20)) define the average location of the centre of gravity for i = 9 and 11 in the x-axis, for i = 3, 5 in the y-axis and the standard deviation of the average brightness of pixels of all objects for i = 9 and 11. The same results of ACC2 were obtained for various configurations of the features w(1), w(2), w(3) and w(4) or w(5), w(7), w(8), w(16) and w(17), which is also quite interesting (Table 2). The best results for the cut decision tree are shown in Table 3. ACC2 = 0.82, obtained for the features w(1), w(4) w(5), w(7) w(8), w(12) and w(15), is the best result for the cut decision tree. The results for the groups Z1, Z2, Z3 confirm that the features from w(17) to w(20) have the smallest influence. The summary frequency chart of the occurrence of individual features from w(1) to w(20) for the cut and complete decision trees for the first 1′000 best results is interesting (Figure 8). It shows the greatest frequency of occurrence of the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. From a practical standpoint, however, minimizing the number of features occurring in the classification seems to be the most vital. Therefore, when analysing only the three top results for the cut decision tree, one feature for Z1, six features Z2 and one feature for Z3 are obtained. This means that only the group Z2 requires the largest number of features in the classification. This information is essential for optimizing the computational complexity of the algorithm. The analysis time for a single image does not exceed one second for the Pentium 4 CPU 3.0 GHz, 8GB RAM.


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 graph of occurrence frequency of individual features w(1) to w(20) for the complete decision tree and the cut one for the first top 1′000 results. The graph shows that the highest occurrence concerns the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. The occurrence frequency of the features corresponds with the results shown in Tables 2 and 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: The graph of occurrence frequency of individual features w(1) to w(20) for the complete decision tree and the cut one for the first top 1′000 results. The graph shows that the highest occurrence concerns the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. The occurrence frequency of the features corresponds with the results shown in Tables 2 and 3.
Mentions: In the classification of groups Z1, Z2, Z3 the features from w(9) to w(12) as well as from w(18) to w(20) did not occur. This means that their influence can be neglected for the best classifications (top 3 results). These features (from w(9) to w(12) as well as from w(18) to w(20)) define the average location of the centre of gravity for i = 9 and 11 in the x-axis, for i = 3, 5 in the y-axis and the standard deviation of the average brightness of pixels of all objects for i = 9 and 11. The same results of ACC2 were obtained for various configurations of the features w(1), w(2), w(3) and w(4) or w(5), w(7), w(8), w(16) and w(17), which is also quite interesting (Table 2). The best results for the cut decision tree are shown in Table 3. ACC2 = 0.82, obtained for the features w(1), w(4) w(5), w(7) w(8), w(12) and w(15), is the best result for the cut decision tree. The results for the groups Z1, Z2, Z3 confirm that the features from w(17) to w(20) have the smallest influence. The summary frequency chart of the occurrence of individual features from w(1) to w(20) for the cut and complete decision trees for the first 1′000 best results is interesting (Figure 8). It shows the greatest frequency of occurrence of the feature w(14) for the complete decision tree and the feature w(4) for the cut decision tree. From a practical standpoint, however, minimizing the number of features occurring in the classification seems to be the most vital. Therefore, when analysing only the three top results for the cut decision tree, one feature for Z1, six features Z2 and one feature for Z3 are obtained. This means that only the group Z2 requires the largest number of features in the classification. This information is essential for optimizing the computational complexity of the algorithm. The analysis time for a single image does not exceed one second for the Pentium 4 CPU 3.0 GHz, 8GB RAM.

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