Hapsari Rinci Kembang, Miswanto Miswanto, Rulaningtyas Riries, Suprajitno Herry, Seng Gan Hong
Department of Informatics, Faculty of Electrical and Information Technology, Institut Teknologi Adhi Tama Surabaya, Indonesia.
Department of Mathematics, Faculty of Sciences and Technology, Universitas Airlangga, Surabaya, Indonesia.
Int J Biomed Imaging. 2022 Apr 20;2022:5336373. doi: 10.1155/2022/5336373. eCollection 2022.
Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.
虹膜具有特定优势，能够记录所有器官状况、身体结构和心理障碍。与疾病引起的器官强度或偏差相关的痕迹会被系统地记录下来，并在虹膜及其周围形成图案。通过图像处理技术可以识别出现在虹膜上的图案。基于虹膜图像中的图案，本文旨在提供一种用于糖尿病（DM）和高血压（HC）早期检测的非侵入性替代方法。在本文中，我们通过在具有256、128、64、32和16个灰度级的量化图像上开发不变的哈拉里克特征，同时对糖尿病和高血压这两种疾病基于虹膜图像进行检测。特征提取过程基于虹膜图像进行早期检测。研究人员和科学家已经介绍了许多方法，其中之一是灰度共生矩阵（GLCM）的特征提取。基于虹膜的早期检测是通过开发体积GLCM（即3D - GLCM）来完成的。基于在距离 = 1且方向为0°、45°、90°、135°、180°、225°、270°和315°形成的3D - GLCM，用于计算哈拉里克特征并开发对量化灰度级数量不变的哈拉里克特征。测试结果表明，灰度级为256的不变特征具有最佳识别性能。在数据集I中，准确率值为97.92，精确率为96.88，召回率为95.83，而在数据集II中，准确率值为95.83，精确率为89.69，召回率为91.67。基于不变特征训练的糖尿病和高血压识别显示出比原始特征更高的准确率。
