© 2017, IJARCSM S All Rights Reserved 125 | P a ge IS SN: 2321- 7782 (Online) e-ISJN: A4372- 3114 Impact Factor: 6.047 Volume 5, Issue 6, June 2017 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com Boundary Refinement Technique Using Laplace Transform for CT Spine Image Segmentation Aditi B. Pardeshi 1 Department of Computer Engineering Late G. N Sapkal College of Engineering, Anjaneri, Nasik – India Prof. J. V. Shinde 2 Department of Computer Engineering Late G. N Sapkal College of Engineering, Anjaneri, Nasik – India Abstract: Image segmentation is designated as partitioning an image into a predetermined number of non-overlapping regions. In medical applications, it is a primary processing most systems that support medical diagnosis, surgical planning and treatments. In general, this procedure is done manually by clinicians, which may be monotonous and time-consuming. To overcome the problem, a number of interactive segmentation methods have been proposed in the literature. But each method has some drawbacks, so we proposed a method which uses prior knowledge of anatomy. In this paper, we propose method to perform segmentation for CT images thoracic and lumbar vertebrae by applying Multiatlas-based segmentation with Laplace Transform. A total 12 CT spine images which consist of thoracic and lumbar vertebrae are used for evaluation. The proposed method is fully automatic as well as efficient. Evaluation of method gives average dice coefficient of 0.90. Keywords: CT, Image segmentation, Label Fusion, Laplace Transform, Multiatlas, Pre-processing, spine, vertebrae. I. INTRODUCTION This Truthful analysis of the spine and spinal structures from medical images is an essential tool in many medical applications of spinal imaging. Study of the detailed shape of individual vertebrae can significantly assist early diagnosis, surgical planning and follow-up opinion of several spinal pathologies, such as spinal deformities, degenerative disorders, tumours and trauma. Also segmentation of individual vertebrae by computer-assisted methods may provide additional support to identification and handling of vertebral fractures. Robust and efficient segmentation algorithms on medical images are a challenging research topic of increasing interest especially from last few years. In case of medical applications, manually segmenting a vertebra is time consuming and tiresome. Therefore semi-automated or fully-automated methods are required for most medical applications which increase the correctness, stability, and reproducibility of the analysis, so that it will allow clinicians to focus more on their other work. The output taken from image segmentation process is the principal parameter for the quality of advance image processing process. Image segmentation algorithms play a fundamental role in medical applications, i.e., diagnosis of diseases related to heart, spine, pelvis, prostate, brain, knee and blood vessel. Therefore, Image segmentation is still a very interesting topic of research for the field of image processing [1]. Atlas is well-defined as the grouping of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template & the target image, the atlas labels are spread to the target image. This process is termed as atlas- based segmentation. In current years, researchers have investigated different registration techniques to align atlases to query subjects and also strategies for atlas construction. The segmentation of the vertebrae is not easy, mainly due to the complex shape, variable architecture of vertebrae across the population and neighbouring anatomy of similar intensity (e.g. other vertebrae, other bones and/or other tissues) [2].A number of segmentation algorithms for computed tomography (CT) images of spine have been proposed. Segmentation of vertebrae was accomplished by unsubstantiated image processing approaches such