Yazar "Ervural S." seçeneğine göre listele

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    Determination of benign and malign lesions by fusion of the different phases of liver MR [Karaci?er MR Görüntülerindeki Farkli Fazlarin Füzyonu ile İyi Huylu ve Kötü Huylu Lezyonlarin Belirlenmesi]
    (Institute of Electrical and Electronics Engineers Inc., 2017) Ervural S.; Ceylan M.
    In this study, different phases of T1-weighted, dynamic contrast-enhanced liver magnetic resonance (MR) images were combined with wavelet-based image fusion to support decisions of radiologists. Used images has labelled as 6 different focal lesion types which focal nodular hyperplasia (FNH), hemangioma, cyst, colangiocellular carcinoma (CCC), hepatocellular carcinoma (HCC) and liver metastases. In application used images are taken by 4 different phases called pre-contrasted, arterial, portal venous, and delay venous from 30 patient. Images registered with efficient subpixel registration by cross correlation method. Discrete wavelet transform(DWT) based image fusion algorithm used and maximum selection method applied as fusion rule. As result 180 fused images obtained The performances of fusion results compared with structural similarity index (SSIM), peak to noise ratio (PSNR) and fusion factor (FF) metrics. In the fusion of portal venous phase and delay venous phase images, 98.7% SSIM and 74.95 dB PSNR values were obtained, respectively. FF value in the fusion of pre-contrast phase & arterial phase images measured as 7.258. In comparison of lesion types were represented with 98.5% SSIM. © 2017 IEEE.
  • Küçük Resim Yok
    Öğe
    Increasing lesion specificity with fusion of manually and automatically segmented liver MR images [Manuel ve otomatik bölütlenmiş karaci?er MR görüntülerinin füzyonu ile lezyon belirginli?inin artirilmasi]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Ervural S.; Ceylan M.
    In this study, it is aimed to analyze the magnetic resonance (MR) images used in the diagnosis of liver focal lesions using image fusion methods and to help diagnosis by adding automatic segmentation results to the manual segmentation process preferred by experts. For this aim fusions of liver MR images, segmented by a fuzzy method and segmented manually. 120 T1-weighted dynamic contrast-enhanced liver MR images of pre-contrast phase, arterial phase, portal vein phase and late venous phase, taken from 30 different patients, were used. Each phase image is also fused with images segmented by the fuzzy c-means algorithm in the same phase, so that the lesion surfaces and contours are displayed on the segmented image manually. Thus, the significance of the lesion was increased before the information in the MR image in which the liver function information was displayed was lost. The resulting new image contains more useful information for automatic decision systems. The results obtained were evaluated using structural similarity index, peak signal-to-noise ratio and fusion factor quality metrics. © 2018 IEEE.