Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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21 Apr 2014
TL;DR: The proposed method is based on non _ subsampled contourlet transform (NSCT) and line detectors and the performance of this method is very promising.
Abstract: We present an effective method for automatically extracting blood vessels from colour images of retinal. The proposed method is based on non _ subsampled contourlet transform (NSCT) and line detectors. In the first step we enhance green channel of retinal image by using the non-subsampled contourlet transform at five levels and 25 directions. This process improves discriminating vessels from background. In the next step we use a line detector at eight scales and twelve directions to extract proper features for detecting vessel centerlines. Finally, vessel width is measured at each pixel on a vessel centerline. The proposed method is evaluated and compared with several recent methods using images from the DRIVE database. As reported the performance of this method is very promising.
8 citations
01 Dec 2010
TL;DR: In this paper, a novel biometric watermarking algorithm that embeds a face image data into fingerprint image is presented, which is based on Contourlet transform and quantization.
Abstract: This paper presents a novel biometric watermarking algorithm that embeds a face image data into fingerprint image. The proposed algorithm is based on Contourlet transform and quantization. TC (Texture Complexity) is computed for selecting the optimum blocks to embed watermarks. The watermarking algorithm is robust to JPEG, Gaussian noise and filtering attacks. It protects the integrity of both the face and fingerprint images. Experimental results performed on the databases of face and fingerprint images show that the algorithm improved security and recognize rate.
8 citations
Journal Article•
TL;DR: The proposed algorithm for SAR image de-noising makes the SAR images to be smoother than Contourlet transform and to be of much fewer man-made textures, the visual effects of the SAR image after de-nosing have been significant improvements.
8 citations
TL;DR: The proposed MRI image based brain tumor retrieval would efficiently deal with a medical decision system based on the CT+ZM fusion method and can yield better result of brain tumor diagnosis in advance where this method using in medical fields.
Abstract: Background: Content based Image Retrieval (CBIR) is employed to search and retrieve the expected image from the database. Magnetic Resonance Imaging (MRI) technique plays a crucial role in diagnosing many diseases in human brain. Methods: In this paper, we proposed a texture fusion technique for T1 and T2 weighted MRI scans. Our proposed technique has three parts. First, texture and shape features are extracted from a brain tumor images. Next, the feature selection techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to combine the texture and shape features. Finally, the popular supervised learning machine techniques like Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are used to classify the brain tumor based on the selected features. Findings: The results of proposed MRI brain tumor diagnosis method are robust, efficient, effective, reduces the retrieval time and improves the retrieval accuracy significantly. Best overall classification accuracy results were obtained using the given DiCom Images. Application: The proposed MRI image based brain tumor retrieval would efficiently deal with a medical decision system based on the CT+ZM fusion method provides more accurate results, so this method can yield better result of brain tumor diagnosis in advance where this method using in medical fields.
8 citations
8 citations