An image processing approach to automatic detection of retina layers using texture analysis

Naseri, Ali; Pouyan, Ali A.; Kavian, Nader.

doi:10.1109/icbme.2010.5704951

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Texture is a result of local variations in brightness within one small region of an image. Naseri et al (2012) described texture as a measure of surface roughness when the intensity values of an image are considered to be elevations.…”

Section: Texture Featuresmentioning

confidence: 99%

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Karthikeyan¹

2014

American Journal of Applied Sciences

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Glaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the preprocessed image as textural features reflects physiological changes in the fundus images. Second order textural features are extracted for different quantization levels namely 8, 16, 32, 64, 128 and 256 in four orientations viz 0, 45, 90 and 135° for various distances. Extracted features are selected using Sequential Forward Floating Selection (SFFS) technique.The selected features are fed to Back Propagation Network (BPN) for classification as normal and abnormal images. The proposed computer aided diagnostic system achieved 96% sensitivity, 94% specificity, 95% accuracy and can be used for screening purposes. In this study, the analysis of gray levels have shown their significance in the classification of glaucoma.

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Section: Texture Featuresmentioning

confidence: 99%

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Karthikeyan¹

2014

American Journal of Applied Sciences

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“…Texture analysis is the description of characteristic of the image properties by textural features (Guo et al, 2020;Liao et al, 2020;Garg & Garg, 2021). Texture is as a result of the local variations in brightness within a region of an image (Naseri et al, 2012;Delibaş & Arslan, 2020;Szychot et al, 2020;Farwell et al, 2021). To extract the texture features from the PolSAR images the Pauli decomposition was used to decomposed the images into |𝑆𝑆 𝐻𝐻𝐻𝐻 + 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝐻𝐻 − 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝑉𝑉 | polarimetric channels.…”

Section: Discussionmentioning

confidence: 99%

Deep support vector machine for PolSAR image classification

Okwuashi

Ndehedehe

Olayinka

et al. 2021

International Journal of Remote Sensing

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The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image classification in remote sensing is the ability to develop classifiers that can substantially discern the different classes inherent in natural and man-made targets. Emphasis has shifted from the use of conventional classifiers to modern non-parametric classifiers such as the Artificial Neural Network (ANN) and Support Vector Machine (SVM); and most recently the hybrid Deep Neural Network (DNN) which is a fusion of Deep Learning (DL) and ANN. This research therefore presents the novel application of Deep Support Vector Machine (DSVM) which is a fusion of DL and SVM to PolSAR image classification. Two PolSAR images of Flevoland region in Netherlands, and Winnipeg in Canada are used as test beds for the experiment. The Lee filter is used to filter the images to suppress the speckle noise in the images. The Pauli decomposition is applied to decompose the images into |𝑆𝑆 𝐻𝐻𝐻𝐻 + 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝐻𝐻 − 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝑉𝑉 | polarimetric channels. Then the Gray Level Co-occurrence Matrix (GLCM) texture feature for |𝑆𝑆 𝐻𝐻𝐻𝐻 + 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝐻𝐻 − 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝑉𝑉 | are extracted based on correlation, contrast, energy, and homogeneity statistics, using GLCM directions 0 0 , 45 0 , 90 0 , and 135 0 with an offset distance of 60. To enhance the efficiency of the model 8, 16, 32, 64, 128, and 256 quantization levels are explored. The DSVM classifier is implemented with four kernel function: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural, and polynomial. The first set of results is a comparison of the DSVM and SVM.

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“…Haralick et al 78 proposed a method for using the GLCM to quantify the spatial relationship between neighbourhood pixels in an image. Haralick features have been successfully used in various application for the analysis of skin cancer and medical image analysis 41,42,[79][80][81][82] . In the current paper, we have extracted the texture features from the probability matrix to classify macular oedema from IR retinal images.…”

Section: Oct Volumesmentioning

confidence: 99%

“…The texture features of the IR eye-fundus images were classified into two groups using an SVM: normal and ME eyes. SVMs are reliable and practical classifiers for small datasets, can be applied in classifications and regression analyses and have been previously used for similar applications 42,44,86 . A linear function was used in this model, and the dataset was divided into training (50%) and test sets (50%).…”

Section: Oct Volumesmentioning

confidence: 99%

“…A texture analysis is performed by extracting the statistical feature sets from the local distributions, which can be used later for segmentation or classification purposes. The gray-level co-occurrence matrix (GLCM) for obtaining the texture features were introduced by Haralick in 1973, and this has been widely used in retinal image analyses 41,42 . Lim et al used a modified combined local binary pattern to extract local gray-level features of all channels and then a support vector machine (SVM) classifier to classify DME.…”

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confidence: 99%

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Infrared retinal images for flashless detection of macular edema

Ajaz

Kumar

2020

Sci Rep

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This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (A Z) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

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An image processing approach to automatic detection of retina layers using texture analysis

Cited by 3 publications

References 16 publications

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Deep support vector machine for PolSAR image classification

Infrared retinal images for flashless detection of macular edema

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