Arab J Sci Eng (2014) 39:1017–1037 DOI 10.1007/s13369-013-0664-4 RESEARCH ARTICLE - ELECTRICAL ENGINEERING A Fast Geodesic Active Contour Model for Medical Image Segmentation Using Prior Analysis and Wavelets Sharif M. S. Al Sharif · Mohamed Deriche · Nabil Maalej · Sami El Ferik Received: 29 November 2011 / Accepted: 4 July 2013 / Published online: 5 September 2013 © King Fahd University of Petroleum and Minerals 2013 Abstract The deformable geodesic active contour (GAC) method is one of the most popular techniques used in object boundary detection in images. In this work, we improve the automatic GAC technique by incorporating prior information extracted from the image region of interest. In addition, we propose a new stopping function to speed up convergence and improve accuracy. The proposed technique was applied to both synthetic and real medical images. The results show both an improvement of more than 40 % in convergence speed together with an excellent accuracy when compared with the previous work. Keywords Deformable models · Geometric active contour (GAC) · Snake method · Boundary detection · Prior information · Medical image segmentation S. M. S. Al Sharif (B ) · S. El Ferik Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia e-mail: sharif182@yahoo.co.uk S. El Ferik e-mail: selferik@kfupm.edu.sa M. Deriche Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia e-mail: mderiche@kfupm.edu.sa N. Maalej Physics Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia e-mail: maalej@kfupm.edu.sa 1 Introduction One of the most important tasks in medical image analysis is segmentation. The main objective is to be able to subdivide a given image into a number of regions exhibiting different properties. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual or functional characteristics [1]. For instance, in therapy planning for cancer patients, this approach is widely used in extracting tumour volumes from medical images [2]. Other medical applications for image segmentation include but are not limited to measuring tissue volumes [3], computer-guided surgery, diagnosis [4], local- ization pathology [5], treatment planning and studying of anatomical structure [6, 7]. 123