Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

MRI Monomodal Feature-Based Registration Based on the Efficiency of Multiresolution Representation and Mutual Information

Abstract: Image registration methods based on mutual information criteria have been widely used in monomodal medi- cal image registration and have shown promising results. Feature-based registration is an efficient technique for clinical use, because it can significantly reduce computational costs. In general, the majority of registration methods consist of the fol- lowing four steps: feature extraction, feature matching, transformation of the models and, finally, resampling the image. It was noted that the accuracy of the registration process depends on matching a feature and control points (CP) detection. Therefore in this paper has been to rely on this feature for magnetic resonance image (MRI) monomodal registration. We have proposed to extract the salient edges and extracted a CP of medical images by using efficiency of multiresolution rep- resentation of data nonsubsampled contourlet transform (NSCT). The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the per- formance of the NTSC and MI similarity measures for 2-D monomodal registration. The experimental results showed that the proposed method produces totally accurate performance for MRI monomodal registration.

Color texture retrieval through contourlet-based hidden Markov model

Abstract: Two statistical models for color texture retrieval based on a hidden Markov model (HMM) in the contourlet domain are described in this paper. Through a contourlet transformation, each color component of an image is decomposed into a set of directional subbands with texture details captured in different orientations. By exploiting inter-scale dependencies and in-band spatial dependencies, the distribution of the coefficients in each subband triplet (subbands of three color components at the same scale with the same orientation) can be estimated using a vector hidden Markov model. The Kullback-Leibler distance (KLD) is used to measure the difference between the distributions of query texture images and those of images in the database. The experimental results show the proposed retrieval systems yield high retrieval rates and better visual quality as compared with previous methods employing hidden Markov models for luminance component alone.

A New Contourlet Based Multiresolution Approximation for MRI Image Noise Removal

Abstract: Modern medical diagnosis equipments included with digital signal processing capabilities have been used for fast and accurate diagnosis of brain structure abnormalities. In this paper a multi resolution based noise removal in magnetic resonance images for abnormality detection and recognition within the brain has been proposed. Wavelet and curvelet based multi resolution approximation has been used to decompose the inter-object relationships into different levels of detail. Contourlet based multi resolution approximation is presented in this work for better abnormality detection. Comparison of extracted feature points between the reference image and the image under study has been made in detection of the abnormality.

Extracting ROI-Based Contourlet Subband Energy Feature from the sMRI Image for Alzheimer's Disease Classification.

Abstract: Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification has attracted a lot of attention and been widely investigated in recent years. However, owing to high dimensionality problem, regions of interest (ROI) of a brain are not characterized properly in spatial domain, which has been a main cause of weakening the discriminating ability of the extracted features. In this study, we propose the ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is firstly segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is separately performed on these ROIs to obtain their subbands. In order to capture energy information, subband energy (SE) feature vector is constructed based on subbands of an ROI. Afterwards, the SE feature vectors of 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, SVM classifier is selected to classify 880 subjects from the ADNI and OASIS databases using the ROICSE feature. Experimental results show that the ROICSE approach outperforms six state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image.

Performance Analysis Of Multi Source Fused Medical Images Using Multiresolution Transforms

Abstract: Image fusion combines information from multiple images of the same scene to get a composite image that is more suitable for human visual perception or further image-processing tasks. In this paper the multi source medical images like MRI (Magnetic Resonance Imaging), CT (computed tomography) & PET (positron emission tomography) are fused using different multi scale transforms. We compare various multi resolution transform algorithms, especially the latest developed methods, such as; Non Subsampled Contourlet Transform, Fast Discrete Curvelet, Contourlet, Discrete Wavelet transform and Hybrid Method (combination of DWT & Contourlet) for image fusion. The fusion operations are performed with all Multi resolution transforms. The fusion rules like local maxima and spatial frequency techniques are used for selection in the low frequency and high frequency subband coefficients, which can preserve more information and quality in the fused image. The fused output obtained after the inverse transform of fused sub band coefficients. The experimental results show that the effectiveness of fusion approaches in fusing multi source images. f n m n m n m