Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.

Autor: Sut SK; Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey., Koc M; Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey., Zorlu G; Department of Biophysics, Faculty of Medicine, Firat University, Elazig, Turkey., Serhatlioglu I; Department of Biophysics, Faculty of Medicine, Firat University, Elazig, Turkey., Barua PD; School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia.; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia., Dogan S; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. sdogan@firat.edu.tr., Baygin M; Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey., Tuncer T; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey., Tan RS; Department of Cardiology, National Heart Centre, Singapore, Singapore.; Duke-NUS Medical School, Singapore, Singapore., Acharya UR; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore.; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore.; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.
Jazyk: angličtina
Zdroj: Journal of digital imaging [J Digit Imaging] 2023 Jun; Vol. 36 (3), pp. 879-892. Date of Electronic Publication: 2023 Jan 19.
DOI: 10.1007/s10278-022-00759-9
Abstrakt: Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.
(© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
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