Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.
DNA microarrays have proved to be one of the vital breakthrough technologies for exploring the patterns of gene expression on a global scale. The differential measured gene-expression levels depend largely on the probe intensities extracted during microarray image processing. Various noises introduced during the experiment and the imaging process can drastically influence the accuracy of results. Microarray image denoising is one of the challenging preprocessing steps in microarray image analysis. In this paper, we propose denoising of microarray images using the independent component analysis (ICA). The idea of ICA i.e. finding the linear representation of nongaussian data so that the components are independent (or atleast as independent as possible) is exploited for denoising microarray images. Through examples, it is shown that the proposed approach is highly effective as compared to the conventional discrete wavelet transform and statistical methods.
KeywordsDenoising, independent component analysis, microarray image, shrinkage function, white Gaussian noise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.