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Cited by 14Image Retrieval and Classification TechniquesArtificial Intelligence in HealthcareRadiomics and Machine Learning in Medical Imaging

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

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Hapsari, Rinci Kembang, Miswanto, Miswanto, Rulaningtyas, Riries, Suprajitno, Herry, Seng, Gan Hong·2022·International Journal of Biomedical Imaging
DOI 10.1155/2022/5336373PMC PMC9045982
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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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  • Haralick's features are a well-established set of rotation invariant image statistics used in medical classification across many fields and imaging modalities.

    “Haralick’s features are a well-established set of rotation invariant image statistics that have been used in medical classification issues across many fields and imaging modalities (Doyle et al, 2008; Gnep et al, 2017; Hapsari et al, 2022).”

    From APOE from astrocytes restores Alzheimer’s Aβ-pathology and DAM-like responses in APOE deficient microglia (2024) · cites this paper as Hapsari et al, 2022

    The Introduction describes Haralick features as GLCM-based texture statistics used for disease classification, and the Discussion reports tests on iris, brain MRI, and chest X-ray images. But the paper’s invariant features are invariant to gray-level quantization, not stated to be rotation invariant (Discussion and Conclusions; page numbers unavailable). (LLM, conf=0.88)

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Cited by

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Authors
Hapsari, R. K., Miswanto, M., Rulaningtyas, R., Suprajitno, H., & Seng, G. H.
Affiliations
  • Institut Teknologi Adhi Tama Surabaya (ID)
  • Universitas Airlangga (ID)
  • Department of Data Science, Universiti Malaysia Kelantan, 16100 UMK City Campus, Pengkalan Chepa, Kelantan, Malaysia
Corresponding author
Rulaningtyas, Riries
ORCID
Venue
International Journal of Biomedical Imaging
Publisher
Wiley
Published
2022-04-20
Volume
2022
Pages
1-11
ISSN
  • 1687-4188 (ISSN-L)
  • 1687-4196
DOI
10.1155/2022/5336373
PMC
PMC9045982
OpenAlex
W4224243354
Cited by
14
References
0
Citations by year
2026: 1 · 2025: 2 · 2024: 3 · 2023: 8
FWCI
1.06
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Biomedical Imaging (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
iris recognition, diabetes mellitus, hypercholesterolemia, early disease detection, GLCM texture features, gray level co-occurrence matrix, gray level quantization, invariant features, non-invasive diagnosis

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