Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features.
Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system based on the global and multi-resolution analysis of retinal...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1184 - 1212 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081746&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081746 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081746 184081746 184081746 10.1007/s10278-024-01243-2 184081746 ppf: 1184 ppct: 28 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features. aug: au: Ahmad, Imtiyaz Singh, Vibhav Prakash Gore, Manoj Madhava affil: https://ror.org/04dp7tp96 Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, 211004, Prayagraj, UP, India sug: subj: Diabetic Retinopathy Diagnosis Diabetic Retinopathy Radiography Diagnosis, Computer Assisted Methods Signal Processing, Computer Assisted Image Interpretation, Computer Assisted Methods Machine Learning Retina Radiography Retina Pathology Tomography, Optical Coherence ab: Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system based on the global and multi-resolution analysis of retinal images. As a first step, we enhance the quality of the retinal images by applying a sequence of preprocessing techniques, which include the median filter, contrast limited adaptive histogram equalization (CLAHE), and the unsharp filter. These preprocessing steps effectively eliminate noise and enhance the contrast in the retinal images. Further, these images are represented at multi-scales using discrete wavelet transform (DWT), and center symmetric local binary pattern (CSLBP) features are extracted from each scale. The extracted CSLBP features from decomposed images capture the fine and coarse details of the retinal fundus images. Also, statistical features are extracted to capture the global characteristics and provide a comprehensive representation of retinal fundus images. The detection performances of these features are evaluated on a benchmark dataset using two machine learning models, i.e., SVM and k-NN, and found that the performance of the proposed work is considerably more encouraging than other existing methods. Furthermore, the results demonstrate that when wavelet-based CSLBP features are combined with statistical features, they yield notably improved detection performance compared to using these features individually. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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