Enhanced Automatic X-Ray Bone Image Segmentation using Wavelets and Morphological Operators S.K.Mahendran 1 and S.Santhosh Baboo 2 1 Assistant Professor, ** 2 Reader, *** Abstract. X-ray bone segmentation is a vital step in X-Ray image analysis techniques. the main aim of segmentation is to subdivide the various portions, so that it can help medical practitioners (i) During the study of bone structure, (ii) Identification of bone fracture, (iii) Measurement of fracture treatment, (iv) Treatment planning prior to surgery. It is considered as a challenging task because the bone x-ray images are complex in nature and the output of segmentation algorithm is affected due to various factors like partial volume effect, intensity inhomogeneity, presence of noise and artifacts and Closeness in gray level of different soft tissues Keywords: X-Ray Bone Segmentation, Long Bone, Tibia, Wavelets, Morphological Operators. 1. Introduction Digital images are increasingly used by medical practitioners to help them during disease diagnosis. These images display various body organs and are used during treatment decision making process. The images are produced by several state-of-the-art medical equipments like MRI, CT, ultrasound and X-Ray. Out of these, X-Ray is one the oldest and frequently used devices, as they are non-intrusive, painless and economical. The X-Ray images are used during various stages of treatment which include fracture diagnosis and treatment, evaluation of skeletal maturation and bone densitometry. X-Ray image analysis techniques to access bone content and bone structure is an area of research that has attracted many researchers [9, 12, 2]. Almost all these techniques, extract features from x-ray images for bone analysis. One important a sub-field of image analysis is the segmentation of bone structure from the x- ray image. Image segmentation is used to classify or assign each pixels into a group (label), where each group represent membership to a set of pixels that define an object or region in the image. Thus, the main aim of segmentation is to subdivide the various portions, so that it can help medical practitioners (i) During the study of bone structure, (ii) Identification of bone fracture, (iii) Measurement of fracture treatment, (iv) treatment planning prior to surgery. It is considered as a challenging task because the bone x-ray images are complex in nature and the output of segmentation algorithm is affected due to various factors like partial volume effect, intensity in homogeneity, presence of noise and artifacts and Closeness in gray level of different soft tissues. The solutions proposed can be categorized into two main groups, namely, gray level feature-based methods and texture feature-based methods. Gray level feature-based methods analyze the gray level or color features of an image to segment an image. Histogram-based methods [01], Edge-based methods [3], region- based methods [13] all belong to this category. Histogram-based methods are the simplest and work with a threshold value. The result of segmentation depends on the correct selection of threshold, which often is difficult. The performance of these methods often degrades in the presence of noise. The edge-based methods works well for noise-free images, but its performance degrades with noisy images or when fake or weak edges are present in the image. ** S.K.Mahendran 1 is Assistant Professor, HOD, Department of Computer Science, Sankara College of Science and Commerce,Coimbatote, Tamil Nadu, India. MobileNumber:91-9842260168; e-mail: sk.mahendran@yahoo.co.in *** Lt.Dr.S.Santhosh Baboo 2 , is Reader in the Postgraduate and Research department of Computer Science at Dwaraka Doss Goverdhan Doss Vaishnav College, Chennai, Tamil Nadu. India. Mobile Number :+91-944-4063888; emaill:santhos2001@sify.com 2011 International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011) © (2011) IACSIT Press, Singapore 125