4 results for “Contourlet”

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Research ArticleOpen accessEvery word in the titleComparative Analysis of Wavelet, Curvelet, and Contourlet Transforms for Denoising Liver CT ImagesMajzoob Kamalaldin Omer AbdalrahmanLiver cancer is one of the leading causes of mortality worldwide, and early detection using CT imaging plays a critical role in patient survival. However, the quality of CT scans is often degraded by noise, particularly in low-dose imaging intended to minimize radiation exposure. This study presents a comprehensive comparison of three multiresolution transform techniques, namely the wavelet, curvelet, and … …Journal of Computer Science, Vol. 22, Issue 1 · 4 Feb 2026 · 2,117 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleCAROTID PLAQUE CLASSIFICATION USING CONTOURLET FEATURES AND SUPPORT VECTOR MACHINESM. Thangavel, M. Chandrasekaran, M. MadheswaranThe aim of this study is to propose a suitable and reliable system for better diagnosis and treatment of carotid diseases. In this study, Computer Aided Diagnosis (CAD) system has been proposed for classifying carotid artery plaques using Contourlet features. Carotid images have been acquired for 124 subjects with symptoms (Amaurosis Fugax, Stroke or Transient Ischemic Attack) and 133 subjects … …Journal of Computer Science, Vol. 10, Issue 9 · 15 Apr 2014 · 6,776 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleMedical Image Compression Using Fuzzy C-Means Based Contourlet TransformM. Tamilarasi, V. PalanisamyProblem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the efficient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image applied with Fuzzy C-means based contourlet transform. DWT applied to the rest … …Journal of Computer Science, Vol. 7, Issue 9 · 25 Jul 2011 · 7,082 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleA robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector MachineJ. S. Leena Jasmine, S. Baskaran, A. GovardhanMammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by … …American Journal of Engineering and Applied Sciences, Vol. 6, Issue 1 · 28 Feb 2013 · 7,856 accessesView PDF Cite