Yazar "Ozturk, Ayse Elif" seçeneğine göre listele

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    DETERMINING THE NUMBER OF TETROMINOE ORDERS FOR DENOISING APPLICATIONS PERFORMED BY TETROLET TRANSFORM
    (IEEE, 2014) Ceylan, Murat; Ozturk, Ayse Elif
    While Tetrolet transform (TT) which is one of the multi-resolution analysis improved recently is applied to images, 5 different shape called tetrominoes is gathered for 4x4 pixel blocks and TT is performed by arranging pixels according to this order. Tetrominoes can be chosen with 117 different combinations. In this study, firstly benchmark images (Lena, Barbara, Boat, Mandrill, Cameraman) and liver MR (magnetic resonance) images are denoised by utilizing TT with 1 to 117 different tetrominoe combinations respectively and optimal number of orders is determined for different noise rates by comparing obtained PSNR (peak signal to noise ratio) results. After that, images are denoised by using just optimal number of tetrominoe orders and all 117 combinations seperately. The operation times are compared. It is clearly seen that using all possible combinations for denoising causes redundant processes and an optimal number of tetrminoe orders can be specified for different image sets according to noise ratio. Decreasing number of combinations shortened the operation time seriously.
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    FUSION AND ANN BASED CLASSIFICATION OF LIVER FOCAL LESIONS USING PHASES IN MAGNETIC RESONANCE IMAGING
    (IEEE, 2015) Ozturk, Ayse Elif; Ceylan, Murat
    Detecting and diagnosing the liver focal lesions have vital importance in planning the treatments of the patients. While there is no need to apply any treatment for benign lesions, medical treatments or surgical operations are necessary in case of existence of malign lesions. Pre-contrast, arterial, portal venous and delayed venous phases in magnetic resonance imaging help to make clear diagnosis through their different contrast material holding properties. In this study, magnetic resonance images belonging to 60 patients are classified as benign/malign by using multi-resolution analysis methods and artificial neural networks. In proposed system, the magnetic resonance images taken from four different phases for each patient are merged with three multi-resolution analyses based on fusion rules and classified by using artificial neural networks. The accuracy rate of the study is reached to 90%.
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    A New Approach for Liver Classification Using Ridgelet/Ripplet-II Transforms, Feature Groups and ANN
    (SPRINGER-VERLAG BERLIN, 2015) Ozturk, Ayse Elif; Ceylan, Murat; Kivrak, Ali Sami
    In this study, 68 liver MR images (28 of them labeled as hemangioma, 40 of them labeled as cyst by specialist radiologists) were classified by using artificial neural network (ANN). Ridgelet transform and its advanced new generation form (called Ripplet-II transform) were applied on these images to compare their effects on liver image classification. Feature vectors were generated by calculating mean, standard deviation, variance, skewness, kurtosis and moment values of coefficient matrices. Firstly, all feature vectors were given as inputs to an ANN and classification process was realized. Then, vectors were seperated into three groups and classified by using three ANNs. This procedure was repeated with two ANNs and two groups of feature vectors. Outputs of the systems which used more than one ANN were evaluated by implementing AND and OR operations seperately. Accuracy, sensitivity and specifity values of obtained results were calculated and compared. The best results were achieved by evaluating the system which used three ANNs and three groups of statistical feature vectors, with AND / OR operations.
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    A New Transform for Medical Image Denoising: Fused Tetrolet Transform
    (AMER SCIENTIFIC PUBLISHERS, 2016) Ozturk, Ayse Elif; Ceylan, Murat
    Denoising is a very important task of medical image processing. Noiseless images provide more accurate diagnosis. Using multi-resolution analysis methods for carrying out medical denoising has become more popular in recent days. Tetrolet transform which is an adaptive form of Haar wavelet transform is one of these methods. In this study, a new form of Tetrolet called "fused Tetrolet transform" is proposed. Denoising performance of this novel method is compared to Wavelet transform, standard Tetrolet transform and four other types of Tetrolet transform. Fused Tetrolet and the modified Tetrolet types are initially used for denoising. In this study, random, gaussian and poisson noises are added on 30 liver magnetic resonance (MR) images and 30 mammography images separately and then removed. Peak signal to noise ratio and structural similarity index are used as evaluation criteria. The results show that fused Tetrolet transform surpasses the four modified forms of Tetrolet, standard Tetrolet and Wavelet while denoising both mammography and liver MR images for all types of noise.