List of Articles Curvelet Transform

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  1 - Improve the Quality of Mammogram Images by Image Processing Techniques
  Mahdi Hariri Hassan Najafy
  Abstract: Due to the spread of breast cancer, its early detection from mammogram images using computerized methods have been considered an effective method to reduce the death rate of patients.In this research, a method based on image processing techniques is presented More

  Abstract: Due to the spread of breast cancer, its early detection from mammogram images using computerized methods have been considered an effective method to reduce the death rate of patients.In this research, a method based on image processing techniques is presented to improve the quality of mammography images. Therefore, in this research, we try to improve the quality of mammography images with image processing techniques to create a medical system. The research has two stages of pre-processing, including equalizing the dimensions and adjusting the histogram of the images, and a stage of feature extraction using Contourlet and Curlet transforms from mammography images, which provides three categories of morphological and histological, statistical, and frequency features to improve diagnosis. And it increases the accuracy of diagnosis. The proposed improvement method was implemented on the MIAS dataset and a subset of the extracted features was selected for the input of the classifier. Comparing the performance of the proposed method on different classifications, this method shows an accuracy rate of 86.3, which is a better result than other methods.MethodThis research is looking for a method that can improve the accuracy of the final diagnosis. Therefore, after the pre-processing stage, which includes rescaling and adjusting the texture of the image, highlighter transforms in the frequency domain such as Curvelet and Contourlet are used to highlight and increase the differentiation of areas with masses in the image for decision-making.Local features based on image zoning are used for image segmentation, and these methods are also used to increase the contrast of mammogram images concerning their surroundings. The improvement methods used in this research use features based on the wavelet domain.ResultsThe input image to the system is subjected to the feature extraction process and three main categories of frequency, morphology, and histology features are extracted from it. This process is done through the cycle, size equalization, histogram adjustment, contourlet transform, and curvelet transform. Due to the sensitivity of the systems, it has been tried to extract the features with various levels and known matrices such as the matrix of events and gray groups.The classification results evaluated the said method. The best results on the data set were the proposed method, which reached an accuracy rate of 86.3 and showed a good improvement on the displayed data set. Manuscript profile
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  2 - Fusion of Synthetic Aperture Radar Images and Optical Images Using Curvelet Transform and Retina model
  Mina Solhi Mehran Yazdi Mahmoud Sharzehei
  In recent years, various image integration techniques have been developed to improve their quality. In this paper, some image integration techniques such as Intensity-Hue-Saturation (HIS), Brovey transform, feedback, non-feedback retina model, wavelet transform, and cur More

  In recent years, various image integration techniques have been developed to improve their quality. In this paper, some image integration techniques such as Intensity-Hue-Saturation (HIS), Brovey transform, feedback, non-feedback retina model, wavelet transform, and curvelet transform are investigated to improve the spectral and spatial information of satellite images. Also, a new algorithm has been proposed to improve the image quality resulting from the combination of SAR and visible-like images. In the proposed method, the curvelet transform is first applied to the three input levels of Synthetic Aperture Radar (SAR) and visible-like images, then using horizontal cells in the feedback retina model, spectral and spatial information below a specified and adjustable frequency is determined by a Gaussian low-pass filter and replaced with the curvelet coefficients of the integrated image approximation sub-band. Moreover, fine1 and detail1 sub-bands are selected from the visible-like image, and the coefficients of fine2, detail2 sub-bands are weighted and aggregated from both SAR and visible-like images in a specific way. Spectral and spatial quality evaluation criteria including Quality Index (Q_I), Measure the Quality of edges (Q^(AB/f)) Relative Dimensionless Global Error in System (ERGAS), Mutual Information (MI), Euclidian Distance (ED)  and Standard Deviation (STD)  were used to compare and analyze the results of the methods. The results of this evaluation indicated the remarkable performance of the proposed method in preserving the spectral and spatial information content of the integrated image compared to other methods. Manuscript profile
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  3 - MRI and PET Image Fusion by Using Curvelet Transform
  Nasrin Amini Emad Fatemizadeh Hamid Behnam
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