Yazar "Yasar, Huseyin" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • Küçük Resim Yok
    Öğe
    An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms
    (SPIE-INT SOC OPTICAL ENGINEERING, 2015) Yasar, Huseyin; Ceylan, Murat
    Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.
  • Küçük Resim Yok
    Öğe
    An Automatic System of Detecting Changes in Aerial Images Using ANN Based Contourlet Transform
    (IEEE, 2015) Yasar, Huseyin; Hatipoglu, Ridvan Safa; Ceylan, Murat
    The obtaining of the aerial images got easy thanks to technological developments in the field of unmanned aerial vehicles and these images were began to be used frequently in the field of image processing. Automatic changes detection from aerial images is among the most important study fields. An automatic system for changes detection has been proposed by using contourlet transform and artificial neural network (ANN) in this study. The contourlet transform is applied to the reference image in the first phase of the system consisting of two phases. Mean, variance, standard deviation and skewness values were calculated from the obtained sub-image matrix and seven image feature vectors are formed by using these statistical values and combinations. The numerical equivalents of the reference image were obtained by using the feature vectors by ANN. The same procedures were applied to the image that its exchange will be examined in the second phase of the system. The change between numerical provisions of the reference image and the image to be examined compared to the threshold value set by the user and automatic changes detection was performed. It was found that the changes in numerical results obtained at the end of the study overlap with the changes in aerial images.
  • Küçük Resim Yok
    Öğe
    Blood Vessel Extraction From Retinal Images Using Complex Wavelet Transform and Complex-Valued Artificial Neural Network
    (IEEE, 2013) Ceylan, Murat; Yasar, Huseyin
    Retinal imaging in ophthalmology plays an important role for the diagnosis of diabetes, cardiovascular disease, etc. In retina images, changes of blood vessels can help the expert to detection of diseases. Manually extraction of blood vessels from retinal images is usually difficult process due to depending on the experience of physician, back-ground artifacts, different acquisition process. Therefore, the aim of this study is to purpose a novel method for automatic blood vessel extraction from retinal image. This study presents a combined structure. This structure is realized with two cascade stages: feature extraction with 4th level Complex Wavelet Transform (CWT) and Complex-Valued Artificial Neural Networks (CVANN) for the blood vessels segmentation. To check the validation of proposed method, public DRIVE database is used. Result of this study has a higher accuracy (98.56 %) than previously studies in the literature.
  • Küçük Resim Yok
    Öğe
    Investigation of Image Representation and Denoising Performances of Real and Complex Valued Fast Finite Shearlet Transform
    (IEEE, 2015) Yasar, Huseyin; Ceylan, Murat
    In this study, image representation and denoising performances of recently defined real and complex valued fast finite shearlet transform are tested as comparatively with real and complex valued fast discrete curvelet transform. End of the study; the results obtained by using complex valued coefficients of the curvelet transform were found to be more successful than the results obtained by using real-valued coefficients. Whereas, obtained results of fast finite shearlet transform for real-valued coefficients are more successful than the obtained results for complex valued coefficients. In the applications, using real-valued fast finite of shearlet transform has achieved the best results.
  • Küçük Resim Yok
    Öğe
    A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Ceylan, Murat; Yasar, Huseyin
    This study determined the features of line, curve, and ridge structures in images using complex ripplet-I and enabled extraction of blood vessel networks from retinal images through a complex valued artificial neural network using those features. Forty color fundus images in the DRIVE database and 20 color fundus images in the STARE database were used to test the success of the proposed system. In this study, a complex version of ripplet-I transform was used for the first time. By presenting the directed image for the determination of the unique geometrical properties of the vessel regions, complex ripplet-I transforms showing better performance than other types of multiresolution analysis were combined with a complex valued ANN. The results in the study were reobtained using leave -one -out cross validation method with bagging technique in order to ensure the stability and correctness of the performance. In the DRIVE database, the highest average accuracy of the system was found to be 98.44% for complex ripplet-I transform and complex valued ANN. For the STARE database (labeled by Adam Hoover), highest average accuracy rates were obtained as 99.25% for complex ripplet-I transforms and complex valued ANN. Similarly, for the other labeled data (by Valentina Kouznetsova), highest average accuracy rates were obtained as 98.03% for complex ripplet-I transforms and complex valued ANN.
  • Küçük Resim Yok
    Öğe
    A Novel Approach for Reduction of Breast Tissue Density Effects on Normal and Abnormal Masses Classification
    (AMER SCIENTIFIC PUBLISHERS, 2016) Yasar, Huseyin; Ceylan, Murat
    Breast tissue density prevents the separation of the abnormal tissue from normal tissue in mammography images due to the negative effects on diagnostic success often hiding abnormalities. In this study, significant reduction of these adverse effects is provided by proposing a complete system including also breast tissue density classification. The breast tissue density type of image, which will first be subjected to normal and abnormal tissue classification, is classified with the proposed system. For the breast tissue density classification, artificial neural network (ANN) and the multiresolution analysis, which were previously proposed in the literature, were used. At the second stage, mammography image was subjected to the classification of normal and abnormal tissue by using trained ANN with the other images which have the same type of breast tissue density according to breast tissue density classification results. Wavelet transform, ridgelet transform and contourlet transform were used at this stage in obtaining image features of mammography. In order to test the success of the proposed system, 265 pieces of region of interests belonging to MIAS database were used. At the end of the study, the highest accuracy is 95.472%, sensitivity is 0.8514, specificity is 1 and A(z) is 0.960. These results can be further increased with semi-automatic operation of the system by performing the classification of breast tissue density by the radiologist. The highest accuracy is 97.736%, sensitivity is 0.9324, specificity is 0.9948 and A(z) is 0.974 for semi-automatic system.