Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.
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 accura...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 879 - 893 |
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| Autores principales: | , , , , , , , , , |
| Formato: | algorithm computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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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=164473088&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473088 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473088 161385426 164473088 164473088 10.1007/s10278-022-00759-9 164473088 ppf: 879 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images. aug: au: Sut, Suat Kamil Koc, Mustafa Zorlu, Gokhan Serhatlioglu, Ihsan Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Acharya, U. Rajendra affil: Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey sug: subj: Automation Adrenal Gland Diseases Classification Tomography, X-Ray Computed Machine Learning Adrenal Glands Pathology Neural Networks (Computer) Pheochromocytoma Adenoma Neoplasm Metastasis Image Enhancement Support Vector Machine Classification ab: 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 × h + k) . It has the potential to be used for screening of adrenal gland disease classes with CT images. pubtype: Academic Journal doctype: algorithm computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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