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首页> 外文会议>International conference on advances in computing, communications and informatics >Detection of high-risk macular edema using texture features and classification using SVM classifier
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Detection of high-risk macular edema using texture features and classification using SVM classifier

机译：使用SVM分类器使用纹理特征和分类检测高风险黄斑水肿

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In digital retinal images, positive means of texture feature detection around the macula region with specified radius is still an open issue. Diabetic macular edema is a complication caused due to Diabetic Retinopathy (DR) and is the true cause of blindness and visual loss. In this paper, we have presented a computerized method for texture feature extraction around the specified radius taking macula as the centre. By proper segmentation techniques, the region of 1DD (Disc Diameter) around the macula centre, was extracted out. The extracted region contained a great amount of abnormalities like micro-aneurysms, hard-exudates and hemorrhages, thereby texture features varied greatly. Unlike other well-known approaches of machine learning classifier techniques, we propose a combination of texture feature extraction from the region of interest around macula and grading using Support Vector Machine (SVM) classifier. The segmented region containing abnormalities differ greatly in texture and a promising “accuracy > 86%” was obtained between the “normal” and “abnormal” type classification. The performance evaluation of the automated system was determined by parameters, namely Sensitivity, Specificity and Accuracy with values obtained about 91%, 75% & 86 % respectively.
机译：在数字视网膜图像中，具有指定半径的黄斑地区周围的纹理特征检测的正方法仍然是一个开放问题。糖尿病黄斑水肿是由于糖尿病视网膜病变（DR）引起的并发症，并且是失明和视觉损失的真正原因。在本文中，我们介绍了一种用于纹理特征提取的计算机化方法，围绕指定的半径呈现黄斑作为中心。通过适当的分割技术，提取出杂散中心周围的1dd（盘直径）的区域。提取的区域含有大量异常，如微动脉瘤，渗出物和出血，从而纹理特征大大变化。与其他知名机器学习分类器技术的方法不同，我们提出了纹理特征提取的组合，从Macula周围的感兴趣区域和使用支持向量机（SVM）分类器进行分级。含有异常的分段区域在质地上大大差异，并且在“正常”和“异常”类型分类之间获得了有希望的“精度> 86％”。自动化系统的性能评估由参数，即敏感性，特异性和准确度决定，分别获得约91％，75％和86％的值。

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