A PARALLEL ROUGH SET BASED SMOOTHING FILTERFOR MEDICAL IMAGE ENHANCEMENT

Abstract

Image processing is a special form of signal processing which provides valuable information toward human image. The most important image processing step in human picture recognition system consists of Edge enhancement process. In this paper we propose a new idea for edge enhancement using smoothing filters based on Rough sets. Rough set theory is an important tool (it is a mathematical tool) that process uncertainty and non-integrity[1][10]. This theory is efficient in analysing and processing inconsistent information, and then detecting connotative information. Rough set theory is to classify the pixel in each part then noise pixels can be separated and removed, which is to provide `better' input for other automated image processing techniques. Smoothing is often used to reduce noise within an image or to produce a less pixel image. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image a good edge enhancement technique is essential. This paper proposes a new idea for enhancement of a medical image using smoothing techniques and Image noise is mostly unwanted and manifested in the pixels of an image and the main application of image averaging is noise removal. In this paper, we deal with Rough set based medical image smoothening filters in order to improve the quality of the image and as well as to help to solve various complex image processing tasks in the future. This paper focuses on an approach which tries to combine the advantages of the various smoothing filters techniques.