Adrenal tumor characterization on magnetic resonance images

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dc.authorid0000-0001-9790-5890
dc.contributor.authorBarstugan, Mucahid.
dc.contributor.authorCeylan, Rahime.
dc.contributor.authorAsoglu, Semih.
dc.contributor.authorCebeci, Hakan.
dc.contributor.authorKoplay, Mustafa.
dc.date.accessioned2020-03-26T20:20:06Z
dc.date.available2020-03-26T20:20:06Z
dc.date.issued2020
dc.departmentSelçuk Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.description.abstractAdrenal tumors occur on adrenal glands and are generally detected on abdominal area scans. Adrenal tumors, which are incidentally detected, release vital hormones. These types of tumors that can be malignant affect body metabolism. Both of benign and malign adrenal tumors can have a similar size, intensity, and shape, this situation may lead to wrong decision during diagnosis and characterization of tumors. Thus, biopsy is done to confirm diagnosis of tumor types. In this study, adrenal tumor characterization is handled by using magnetic resonance images. In this way, it is wanted that patient can be disentangled from one or more imaging modalities (some of them can includes X-ray) and biopsy. An adrenal tumor image set, which includes five types of adrenal tumors and has 112 benign tumors and 10 malign tumors, was used in this study. Two data sets were created from the adrenal tumor image set by manually/semiautomatically segmented adrenal tumors and feature sets of these data sets are constituted by different methods. Two-dimensional gray-level co-occurrence matrix (2D-GLCM), gray-level run-length matrix (GLRLM), and two-dimensional discrete wavelet transform (2D-DWT) methods were analyzed to reveal the most effective features on adrenal tumor characterization. Feature sets were classified in two ways: benign/malign (binary classification) and type characterization (multiclass classification). Support vector machine and artificial neural network classified feature sets. The best performance on benign/malign classification was obtained by the 2D-GLCM feature set. The best results were assessed with sensitivity, specificity, accuracy, precision, and F-score metrics and they were 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. The highest classification performance on type characterization was obtained by the 2D-DWT feature set as 59.62%, 96.17%, 93.19%, 54.69%, and 54.94% for sensitivity, specificity, accuracy, precision, and F-score metrics, respectively.en_US
dc.identifier.citationBasrtugan, M., Ceylan, R., Asoglu, S., Cebeci, H., Koplay, M. (2020). Adrenal Tumor Characterization on Magnetic Resonance Images. International Journal of Imaging Systems and Technology, 30(1), 252-265.
dc.identifier.doi10.1002/ima.22358en_US
dc.identifier.endpage265en_US
dc.identifier.issn0899-9457en_US
dc.identifier.issn1098-1098en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage252en_US
dc.identifier.urihttps://dx.doi.org/10.1002/ima.22358
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38501
dc.identifier.volume30en_US
dc.identifier.wosWOS:000479373900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAsoglu, Semih.
dc.institutionauthorCebeci, Hakan.
dc.institutionauthorKoplay, Mustafa.
dc.language.isoenen_US
dc.publisherWILEYen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectadrenal glandsen_US
dc.subjectadrenal tumor classificationen_US
dc.subjectfeature extractionen_US
dc.subjectMR imagesen_US
dc.subjectsegmentationen_US
dc.titleAdrenal tumor characterization on magnetic resonance imagesen_US
dc.typeArticleen_US

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