FUZZY ENHANCEMENT TECHNIQUE USING S-MEMBERSHIP FUNCTION IN MEDICAL APPLICATIONS M. SUNEEL 1 , K. SAMBA SIVA RAO 2 , M. LAVANYA 3 & M. SAI SASANKA 4 1 Lecturer, ECE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India 2 Assistant Professor, ECE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India 3 ECE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India 4 ECE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India ABSTRACT This paper presents a new image enhancement method through fuzzy logic based S-shaped membership function for contrast enhancement of X-ray images in medical diagnosis. The main idea is to enhance the tissues and also smooth the distorted regions adaptively from a proposed Fuzzification technique using S-shaped Membership function. Firstly, we apply the median filtering operation for reducing unwanted noise contents and sequentially the transformation operation. Experimental results show that the proposed method can enhance tissues brightness level for better image quality. KEYWORDS: Image Enhancement, Fuzzy Logic, Membership function, X-Ray Images INTRODUCTION It is a well-known fact that an image gets degraded adversely either during its capturing by a camera or its transmission for further processing by means of a digital computer. For instance, the quality of an image captured by means of a digital camera may sometimes become low due to imperfections in the camera‟s lens system, the relative motion between the target and the camera and other factors such as environmental changes. Therefore, it becomes inevitable that the quality of the image should be improved in order to select some interested image features. To accomplish this we need image enhancement technique that not only removes the unknown degradation in the image but also makes the image visually good. There are many image enhancement methods such as histogram equalization (or linearization), histogram matching, image smoothening and image sharpening. The main aim to enhance X-ray image is that it is a major medical diagnostics due to its non-invasive and non radiation properties. The problem with this technology is low resolution and high noise, making the pictures difficult to read and diagnose. Acoustic View has a goal to make X-ray image as a leading diagnostic by enhancing its resolution and reducing the noise associated with the images. Clinicians are becoming increasingly reliant on X-ray techniques due to some advantages, such as convenient, non-invasive and real-time scanning. Recently, technical advances in diagnostic ultrasound can help clinicians diagnose unknown disease with the visualized information in patients' anatomy. Specifically, the technique has been valuable potential for the examinations in maternal fatal bonding or gynaecology problems. However, the aforementioned traditional image enhancement methods yield undesired results especially when the images to be processed are rich in uncertainties and inaccuracies. Since, Fuzzy Logic representations based on fuzzy set theory try to capture the way humans represent and reason with real-world knowledge in the face of uncertainty they have attracted the attention of many researchers. A fuzzy set can be defined mathematically by assigning to each possible individual in the universe of discourse, a value representing its grade of membership in the fuzzy set. Though fuzzy enhancement technique yields good results in terms of enhancement, the dynamic range of the gray levels in the output International Journal of Electronics and Communication Engineering (IJECE) ISSN 2278-9901 Vol. 2, Issue 2, May 2013, 121-126 © IASET