GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with many local optima. To circumvent this problem, we propose Niching Genetic K-means Algorithm (NGKA) that is based on modified deterministic crow...

A feature-preserving non-local means (FP-NLM) filter has been developed recently for denoising images containing small and weak particlelike objects. It explores the commonly used non-local means filter to employ two similarity measurements taken in the original greyscale image and a feature image which measures the particle probability in the original image. In this paper, we report a new appr...

Among various kinds of image denoising methods, the Perona–Malik model is a representative Partial Differential Equation based (PDE-based) algorithm which effectively removes the noise as well as having edge enhancement simultaneously through anisotropic diffusion controlled by the diffusion coefficient. However, the unstable behavior of the Perona–Malik model introduces staircasing artifacts i...

In this paper, we present the use of a generic image segmentation method, namely a succession of Random Forest classifiers in an autocontext framework, for the MICCAI 2014 Challenge on Endocardial 3D Ultrasound Segmentation (CETUS). The proposed method segments each frame independently in 90 sec, without requiring temporal information such as end-diastolic or end-systolic time points nor any re...

This paper introduces a novel method for noise reduction in medical images based on concepts of the Non-Local Means algorithm. The main objective has been to develop a method that optimizes the processing speed to achieve practical applicability without compromising the quality of the resulting images. A database consisting of prototypes, composed of pixel neighborhoods originating from several...

In this article, we propose a super-resolution method to resolve the problem of image low spatial because of the limitation of imaging devices. We make use of the strong nonlinearity mapped ability of the back-propagation neural networks(BPNN). Training sample images are got by undersampled method. The elements chose as the inputs of the BPNN are pixels referred to Non-local means(NL-Means). Ma...

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and optimizes a weighted version of the k-Mean...

In this paper, a novel algorithm for content-based video indexing and retrieval using key-frames texture, edge, and motion features is presented. The algorithm extracts key frames from a video using k-means clustering based method, followed by extraction of texture, edge, and motion features to represent a video with the feature vector. The algorithm is evaluated on a database of three hundred ...

In this paper, we propose a fast algorithm called PatchLift for computing distances between patches extracted from a one-dimensional signal. PatchLift is based on the observation that the patch distances can be expressed in terms of simple moving sums of an image, which is derived from the one-dimensional signal via lifting. We apply PatchLift to develop a separable extension of the classical N...

Patch-based methods used in digital image processing fields are generally able to produce effective results. Although these approaches use easier structures to achieve better visual quality in digital image restoration compared with other methods, research is still going on in the field. In this study, a better noise reduction approach is presented using a patch-based algorithm in the wavelet d...