Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Publisher: search.filters.publisher.IEEE345 E 47TH ST, NEW YORK, NY 10017 USA

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  • Conference Object
    Effect of Bilinear Interpolation on the Texture Analysis of Colonoscopy Images
    (IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Kacmaz, Rukiye Nur; Yilmaz, Bulent
    Interpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32x32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections.
  • Conference Object
    Citation - WoS: 8
    Citation - Scopus: 9
    Computer-Aided Classification of Breast Cancer Histopathological Images
    (IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017-10) Aksebzeci, Bekir Hakan; Kayaalti, Omer
    Nowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-assisted diagnosis of breast cancer is a promising field.