An efficient pipeline for abdomen segmentation in CT images

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dc.authorid0000-0003-4541-8833
dc.contributor.authorKoyuncu, Hasan.
dc.contributor.authorKoyuncu, Hasan.
dc.contributor.authorSivri, Mesut.
dc.contributor.authorErdogan, Hasan.
dc.date.accessioned2020-03-26T19:52:51Z
dc.date.available2020-03-26T19:52:51Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractComputed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.en_US
dc.identifier.citationKoyuncu, H., Ceylan, R., Sivri, M., Erdogan, H. (2018). An Efficient Pipeline for Abdomen Segmentation in CT Images. Journal of Digital Imaging, 31(2), 262–274.
dc.identifier.doi10.1007/s10278-017-0032-0en_US
dc.identifier.endpage274en_US
dc.identifier.issn0897-1889en_US
dc.identifier.issn1618-727Xen_US
dc.identifier.issue2en_US
dc.identifier.pmid29067570en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage262en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s10278-017-0032-0
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36313
dc.identifier.volume31en_US
dc.identifier.wosWOS:000428438400015en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKoyuncu, Hasan.
dc.institutionauthorKoyuncu, Hasan.
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF DIGITAL IMAGINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAbdomen segmentationen_US
dc.subjectEdge detectionen_US
dc.subjectComputed tomographyen_US
dc.subjectStatistical pipelineen_US
dc.subjectImage registrationen_US
dc.titleAn efficient pipeline for abdomen segmentation in CT imagesen_US
dc.typeArticleen_US

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