Septiarini Anindita, Khairina Dyna M, Kridalaksana Awang H, Hamdani Hamdani
Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Samarinda, Indonesia.
Healthc Inform Res. 2018 Jan;24(1):53-60. doi: 10.4258/hir.2018.24.1.53. Epub 2018 Jan 31.
Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, the number of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statistical features and the k-nearest neighbor algorithm as the classifier.
We propose three statistical features, namely, the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features are obtained through feature extraction followed by feature selection using the correlation feature selection method. To classify those features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images.
To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consisting of 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy, and the overall result achieved in this work was 95.24%, respectively.
This research showed that the proposed method using three statistics features achieves good performance for glaucoma detection.
青光眼是一种无法治愈的眼部疾病，是全球第二大致盲原因。据估计，到2020年，该疾病的患者数量将会增加。本文提出了一种利用统计特征和k近邻算法作为分类器的青光眼检测方法。
我们提出了三种统计特征，即均值、平滑度和三阶矩，这些特征是从视神经乳头图像中提取的。这三种特征通过特征提取获得，然后使用相关特征选择方法进行特征选择。为了对这些特征进行分类，我们应用k近邻算法作为分类器对眼底图像进行青光眼检测。
为了评估所提方法的性能，使用了84张眼底图像作为实验数据，其中包括41张青光眼图像和43张正常图像。我们所提方法的性能通过准确率来衡量，在这项工作中取得的总体结果分别为95.24%。
本研究表明，所提的使用三种统计特征的方法在青光眼检测中取得了良好的性能。
