Yazar "Alpaslan N." seçeneğine göre listele

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    Classification of breast masses in mammogram images using KNN
    (Institute of Electrical and Electronics Engineers Inc., 2015) Alpaslan N.; Kara A.; Zencir B.; Hanbay D.
    Breast cancer is one of the most deadly diseases for women. Mammogram is very important imaging technique used diagnosis in early stages of breast cancer. In this study, a decision support system which helps experts to examine mammogram images in the fight against breast cancer is developed. In this study, firstly several preprocesses are applied to mammogram to make image clear and segmentation of mass is provided with an appropriate threshold value. After the segmentation processes, features of the tumor mass are obtained. The obtained features are classified as normal, benign or malignant using kNN (k-nearest neighbours) classifiers. In this study, its have been were shown that, effect of kurtosis, skewness and wavelet energy features on classification performance is shown. As a result, it has been seen that, these features improve the classification performance. © 2015 IEEE.
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    Computer-aided tumor detection system using brain MR images
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ari A.; Alpaslan N.; Hanbay D.
    In today's technology, computer assisted detection applications have managed to make great contributions to the field of medicine. Computer assisted detection systems aim to help radiologist about mass detection by using image processing systems. In this study, it's aimed to carry out mass detection process on the images of 3D brain MRI (Magnetic Resonance Imaging). The steps followed in this study are the stage of pre-processing the stage of segmentation, identification of the areas of interests and identification of tumor. As a result of processing's in the stages of preprocessing and segmentation, obtained areas of interests are labelled and attributes of these areas of interests are extracted during the stage of attributes extraction and in the last stage, the areas of interests are identified as whether they are mass or not according to these attributes. With this method applied on 845 number of magnetic resonance image sections belonging to 13 patients, it has been achieved classification success with 86.39%. © 2015 IEEE.
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    A novel texture classification method based on Hessian matrix and principal curvatures
    (IEEE Computer Society, 2014) Alpaslan N.; Hanbay K.; Hanbay D.; Talu M.F.
    In this study, in order to obtain similar effect with conventional gradient operation and extract more robust feature for texture, we use the principal curvature informations instead of the gradient calculation. Through this methods, sharp and important informations about the texture images were obtained by analyzing images of the second order. Considering the classification results obtained, it is shown that the proposed method improve the performance of original CoHOG and HOG feature extraction methods. As a result of experiments on datasets with different characteristics, it is seen that, the proposed method has higher classification performance. © 2014 IEEE.