A Hybrid Tool on Denoising and Enhancement of Abdominal CT Images before Organ & Tumour segmentation

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dc.contributor.authorKoyuncu, Hasan
dc.contributor.authorCeylan, Rahime
dc.date.accessioned2020-03-26T19:33:28Z
dc.date.available2020-03-26T19:33:28Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description37th IEEE International Conference on Electronics and Nanotechnology (ELNANO) -- APR 18-20, 2017 -- Kyiv, UKRAINEen_US
dc.description.abstractMost of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement parts are crucial for better segmentation results on CT images. In this paper, we form a tool including three efficient algorithms for the purpose of image enhancement before abdominal organ & tumour segmentation. At first, the denoising process is realized by Block Matching and 3D Filtering (BM3D) algorithm for elimination of Gaussian noise stated in arterial phase CT images. At second, Fast Linking Spiking Cortical Model (FL-SCM) is used for removing the internal fat tissue. At last, Otsu algorithm is processed to remove the redundant parts within the image. In experiments, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index are used to evaluate the performance of proposed method, and a visual comparison is presented. According to results, it is seen that proposed tool obtains the best PSNR and SSIM values in comparison with two steps of pipeline (FL-SCM and BM3D & FL-SCM). Consequently, BM3D & FL-SCM & Otsu (BFO) ensures a clean abdomen particularly for segmentation of liver, spleen, pancreas, adrenal tumours, aorta, ribs, spinal cord and kidneys.en_US
dc.description.sponsorshipIEEE, IEEE Ukraine Sect, IEEE EMBS Ukraine Chapter, IEEE Ukraine Sect IE IA PE Soc Joint Chapter, IEEE KPI Student Branch, IEEE Ukraine AES SP Joint Chapter, Natl Acad Sci Ukraine, Inst Microdevices, Natl Acad Sci, V Ye Lashkaryov Inst Semiconductor Phys Ukraine, youngprofessionals, IEEE E Ukraine AP MTT ED AES GRS NPS Soc Joint Chapter, Natl Aviat Univ, IEEE Cent Ukraine ED MTT COM CPMT SSC Soc Joint Chapter, Teleopt PRA Ltd, Natl Tech Univ Ukraine, Kyiv Polytechn Insten_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.endpage254en_US
dc.identifier.isbn978-1-5386-1701-4
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage249en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34723
dc.identifier.wosWOS:000403399800054en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 IEEE 37TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectDenoisingen_US
dc.subjectImage Enhancementen_US
dc.subjectOrgan & Tumour Segmentationen_US
dc.subjectComputed Tomographyen_US
dc.subjectAbdominalen_US
dc.titleA Hybrid Tool on Denoising and Enhancement of Abdominal CT Images before Organ & Tumour segmentationen_US
dc.typeConference Objecten_US

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