Image Enhancement of Normal and Abnormal with Edema Segmentation of Brain MR Images Using Adaptive Wiener Filter

In this paper, we are present the efficient technique for the segmentation of normal, abnormal with tumor and edema tissues in the MR brain images. The normal brain tissues such as WM (White Matter), GM (Gray Matter) and CSF (Cerebrospinal Fluid) are segmented by using classical approach like gradient and binarization methods. In medical diagnosis it is very important to identify tissues which are infected and same equal importance to the tissues with are going to be damaged in due time like edema tissues. In this process of segmentation preprocessing is applied to eliminate non-cortical tissues using skull stripping with region growing method for a better focus on cortical tissues. The proposed method shows the best cortical tissue segmentation compare with the existing techniques. The same approached can also be used for efficient segment in noisy conditions using non-linear Adaptive Wiener filter. Performance of the proposed approach is compared against the Weiner filter and adaptive Weiner filter in terms of peak-signal-to-noise-ratio (PSNR) and Mean squared error (MSE) on real MR images collected from national brain research centre. Key-words: Edema, MR brain images, WM,GM,CSF,PSNR,MSE. 1.INTRODUCTION The segmentation of brain tumor from magnetic resonance (MR) images is a vital process for treatment planning and for studying the differences of healthy subjects and subjects with tumor. Brain MRI with tumor is difficult to segment due to a combination of the following factors: 1. The deformation of non-tumor structures due to tumor mass effect. 2. Infiltration of brain tissue by tumor and edema (swelling). Edema appears around the tumor mainly in white matter regions. 3. There is gradual transition from tumor to edema, often it is difficult to discern the boundary between the two structures.4. The standard MR modality used to identify tumor, T1w with contrast enhancement (typically using gadolinium), is not always ideal. Blood vessels and cortical cerebrospinal fluid (csf) tend to be highlighted along with tumor, while parts of tumor that are necrotic tissue do not appear enhanced at all. It is generally impossible to segment tumor by simply thresholding the contrast enhanced T1w image. It is one of the most vital reprocessing steps in several medical research and clinical applications, such as quantification of tissue volume, visualization and analysis of anatomical structures, multimodality fusion and registration, functional brain mapping, identification of pathology, surgical planning, surgical navigation, and brain substructure segmentation [1]. Segmentation at preliminary stage is important and necessary for the analysis of medical images for computer-aided diagnosis and treatment. As the images are inherent in nature, medical image segmentation is a difficult and challenging task [2] [14] [15]. Magnetic resonance imaging (MRI) is a significant diagnostic imaging method, which is employed for the early detection of abnormal changes in tissues and organs [3] [16] as well as it is a non-invasive imaging technique, so it allows a radiologist to create an image of the inner aspects of living tissue [12]. Normally the structure of brain is complex and its accurate segmentation is very crucial for finding the tumors, edema and necrotic tissues in order to specify proper therapy [4]. The brain matters are mainly categorized as white matter, gray matter, cerebrospinal fluid (CSF) or vasculature. Mostly the brain structures are clearly described by the boundaries of the tissue classes, so a technique to segment tissues based on these categories is a major step in quantitative morphology of brain [6]. Apart from other diagnostic methods, Magnetic resonance imaging (MRI) systems can generate many images and each image indicates a different essential parameter of inner anatomical structures in the same body section with multiple differences, based on the local variations of spin–spin relaxation time (T2), spin–lattice relaxation time (T1), and proton density (PD) [5]. The presence of noise, errors in the scanners, and the structural variations of the imaging objects are the major obstruction to the segmentation of MR images, such obstructions are categorized into four types: thermal/electronic noise, magnetic field inhomogeneities, biological tissue variations, and incomplete volume effects [7]. Moreover, recognition and analysis of the lesions manually Moreover, recognition and analysis of the lesions manually from MR brain images are generally time consuming, expensive and can produce unacceptably high intra observer and inter observer variability [8]. The segmented MR images used in the medical diagnostic process depends on a combination of two, often conflicting, requirements, that is, the removal of the unnecessary information present in the original MR images and the maintenance of the significant details in the resulting segmented images [13] [17]. MR-image segmentation methods are usually evaluated based on their ability to A.Ramaswamy Reddy et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3) , 2012,4176 4181