Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2022-04-20 DOI:10.1155/2022/5336373
R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan
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引用次数: 1

Abstract

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 d = 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.
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改进灰度Haralick纹理特征用于虹膜图像早期检测糖尿病和高胆固醇
虹膜具有特定的优势,可以记录所有器官状况、身体结构和心理障碍。与疾病引起的器官强度或偏差有关的痕迹被系统地记录下来,并在虹膜及其周围形成图案。虹膜上出现的图案可以通过使用图像处理技术来识别。基于虹膜图像中的模式,本文旨在为糖尿病和HC的早期检测提供一种替代的非侵入性方法。在本文中,我们通过在具有256、128、64、32和16灰度级的量化图像上发展不变的Haralick特征,同时基于虹膜图像对DM和HC这两种疾病进行检测。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使用体积GLCM开发完成的,即3D-GLCM。基于距离d=1、方向为0°、45°、90°、135°、180°、225°、270°和315°的3D-GLCM,它被用来计算Haralick特征,并发展出对量化灰度级数量不变的Haralick特性。测试结果表明,灰度为256的不变特征具有最好的识别性能。在数据集I中,准确度值为97.92,准确度为96.88,召回率为95.83;而在数据集II中,准确率值为95.83,准确度89.69,召回率91.67。在不变特征上训练的DM和HC的识别显示出比原始特征更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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