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Umadevi, N.
- Analysis of the Application of Bi-Orthogonal Wavelet Transform, Bayesthresholding and Independent Component Analysis (ICA) on Poisson Noise Removal from X-Ray Images
Authors
1 Sri Jayendra Sarawsathi College of Arts & Science, Coimbatore, IN
2 Avinashilingam Deemed University, Coimbatore, IN
Source
Digital Image Processing, Vol 3, No 6 (2011), Pagination: 353-359Abstract
Medical field produces huge volume of images, which are used during disease diagnosis. X-Rays are the oldest and most frequently used form of medical imaging. These images are used in many applications with prominent use found in fracture detection. The X-Ray images are normally affected by Poisson noise, which degrades the visual quality of the image and obscures important information required for accurate diagnosis. The current need is, thus, a method that removes noise while preserving important diagnostic data. This study proposed a method that combines Multiple Wavelet Denoising (MWD) Structure with ICA to remove Poisson noise from X-Ray images. The thresholding method used is Bayes Shrink and both soft, hard thresholding methods are analyzed. From the experimental results, it is evident that the proposed model produces images, which are visually clean and smooth, in fast manner. At the same time, the proposed method also preserves edges and other significant details of the image.Keywords
Multiple Wavelet Denoising (MWD), Bi-Orthogonal Wavelet Transform, Bayesthresholding and Independent Component Analysis (ICA), Poisson Noise.
- Content Based Medical Image Retrieval-A Study
Authors
1 Avinashilingam University for Women, Coimbatore–641 043
2 S. Avinashilingam University for Women, Coimbatore–641 043, IN
Source
Digital Image Processing, Vol 1, No 6 (2009), Pagination: 248-256Abstract
Image retrieval is a technique to find similar images from an image archive by their textual or visual contents. Images can be retrieved from huge databases either by using text annotations or by analyzing the content of the images, in which case it is called Content Based Image retrieval (CBIR). CBIR can be applied to various applications like the Internet, healthcare industries, etc. CBIR is considered challenging in the medical field because the characteristics of medical images differ significantly from other general-purpose images. In the last decade, content-based image retrieval methods have been widely studied in different application domains and particularly, research in the medical field has taken special interest. This paper presents a highlight of recent researches in Content Based Medical Image Retrieval (CBMIR), techniques and trends, key issues and limitations.