Research ArticleOpen accessPhrase in the titleImage Thresholding Using the Complement FeatureSalah AmeerA new feature (the complement feature) is proposed in an Eigen formulations for performing global image thresholding. The goal is to find an intensity or gray-level value below which is the background while above it is the foreground (object). Each pixel in the image is represented by a (2D) unit vector where the x-component is the normalized (to [0,1] or … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 2 · 8 Jun 2020 · 6,505 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleMulti-Level Image Thresholding via Nonlinear Fitting of the HistogramSalah AmeerThis paper proposes a novel scheme for multi-level image thresholding. The 1D cumulative histogram is curve-fitted to a polynomial of nonlinear basis function, the hyperbolic tangent (tanh). The thresholds are the positions of the minima of the first (odd) derivative of the fitting polynomial, since the histogram is the derivative of the cumulative histogram. The tanh function is considered as … …American Journal of Applied Sciences, Vol. 16, Issue 12 · 28 Dec 2019 · 7,533 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleHistogram Matching Schemes for Image ThresholdingSalah AmeerThis paper proposes several novel schemes for image thresholding. The idea is simply to compare the original image histogram to that of the thresholded image. Element by element comparison (sum of absolute difference between the two histograms) is found to be of better performance than a single feature (area or size) comparison. The optimum threshold is the one producing the … …American Journal of Engineering and Applied Sciences, Vol. 12, Issue 3 · 9 Sep 2019 · 7,872 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleMultilevel Image Thresholding Using the Complement FeatureSalah AmeerA novel Eigen formulation is proposed for image segmentation. Each pixel is represented by a unit vector having the x-component as the normalized gray value of the pixel. The axes of inertia are simply the Eigen vectors of the auto-correlation matrix. The largest Eigen vector is used as the point of split. Each sub-image is further split in the same … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 3 · 28 Aug 2020 · 5,917 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAn Adaptive Detail Preserving Modified Progressive Pca Thresholding Based Image DenoisingN. Anilkumar, S. Radhakrishnan, Shaik Syed Basha et al.Radiologists use medical imaging tools to assess the anatomy and function of interior organs. However, medical images are frequently distorted by various types of noise generated during the capture, transmission, and reconstruction operations, which can dramatically reduce image quality and hide critical diagnostic features. As a result, developing effective denoising algorithms that suppress noise while maintaining structural information remains a … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 529 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Detailed Preserving Medical Image Denoising Using Cluster-Wise PCA Thresholding and Iterative Mean FilteringMohit Sharma, Ayush Dogra, Anita Gupta et al.Image denoising is a vital process in medical imaging that involves removing noise or distortions introduced during image acquisition. Random noise can degrade image quality and reduce contrast, making accurate analysis more challenging. This paper presents a detail-preserving medical image denoising framework based on a hybrid methodology that combines cluster-wise Principal Component Analysis (PCA) thresholding with Iterative Mean Filtering (IMF). … …Journal of Computer Science, Vol. 22, Issue 4 · 18 Apr 2026 · 1,787 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleModified Curvelet Thresholding Algorithm for Image DenoisingAl-Dahoud Ali, Preety D. Swami, J. SinghaiProblem statement: This study introduced an adaptive thresholding method for removing additive white Gaussian noise from digital images. Approach: Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet transform methods which being nongeometrical lack sparsity and fail to show optimal rate of convergence. Results: Different behaviors of curvelet transform maxima of image and noise … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,255 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComparative 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 PDFCite
Research ArticleOpen accessIn the keywordsLocal Features Supported by the Complement Feature for Image SegmentationSalah AmeerAn Eigen formulation is proposed for image thresholding/segmentation. A vector composed of local features, normalized intensity of each pixel and that of the neighboring pixels, is used to represent each pixel. A “complement” component is appended to this vector to produce a “unit” vector. The auto-correlation matrix is computed for each pixel in the image using this unit vector. The … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 3 · 7 Jul 2020 · 6,602 accessesView PDFCite
Review ArticleOpen accessIn the keywordsStatistical Binarization Techniques for Document Image AnalysisSaad M. Ismail, Siti Norul Huda Sheikh Abdullah, Fariza FauziBinarization is an important process in image enhancement and analysis. Currently, numerous binarization techniques have been reported in the literature. These binarization methods produce binary images from color or gray-level images. This article highlights an extensive review on various binarization approaches which are also referred to as thresholding methods. These methods are grouped into seven categories according to the employed … …Journal of Computer Science, Vol. 14, Issue 1 · 3 Jan 2018 · 8,160 accessesView PDFCite