Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images
Article
Sut, Suat Kamil, Koc, Mustafa, Zorlu, Gokhan, Serhatlioglu, Ihsan, Barua, Prabal Datta, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Tan, Ru‑San and Acharya, U. Rajendra. 2023. "Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images." Journal of Digital Imaging. 36 (3), pp. 879-892. https://doi.org/10.1007/s10278-022-00759-9
| Article Title | Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images |
|---|---|
| ERA Journal ID | 35211 |
| Article Category | Article |
| Authors | Sut, Suat Kamil, Koc, Mustafa, Zorlu, Gokhan, Serhatlioglu, Ihsan, Barua, Prabal Datta, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Tan, Ru‑San and Acharya, U. Rajendra |
| Journal Title | Journal of Digital Imaging |
| Journal Citation | 36 (3), pp. 879-892 |
| Number of Pages | 14 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 0897-1889 |
| 1618-727X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10278-022-00759-9 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s10278-022-00759-9 |
| Abstract | 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 \(O(w \times h+k)\). It has the potential to be used for screening of adrenal gland disease classes with CT images. |
| Keywords | Adrenal gland; Center symmetric local binary pattern; Neighborhood component analysis ; Classification |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Adiyaman Training and Research Hospital, Turkiye |
| Firat University, Turkey | |
| School of Business | |
| University of Technology Sydney | |
| Ardahan University, Turkiye | |
| Duke-NUS Medical Centre, Singapore | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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