Digital Image Processing

A. Rajendran ¹, R. Dhanasekaran ²

Affiliations
1 RVS College of Engineering & Technology, ECE Department, Coimbatore, IN
2 Syed Ammal Engineering College, Ramanathpuram, Coimbatore, IN

Abstract

Medical image segmentation is the most important process and research focus in the medical image processing field. Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. In this paper the gradient vector flow (GVF) snake model is modified with thinning canny edge detection is used for brain tumor segmentation. The thinning canny operator is used to calculate the edge map gradient for GVF snake model. Then the GVF deforms with initial contour. Simulation results show that the GVF model with thinning canny operator can extract the boundary of brain tumor accurately. This method can overcome the problem that traditional snake cannot have efficient converge to the weak boundary.

Keywords

GVF Snake, Segmentation, Brain Tumor, Deformable Model.

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A. Rajendran ¹, R. Dhanasekaran ²

Affiliations
1 RVS College of Engg. &Technology, ECE Department, Coimbatore, IN
2 Department of ECE, Nehru Institute of Engineering and Technology, Coimbatore, IN

Abstract

Image segmentation is one of the primary steps in image analysis for object identification. The main aim is to recognise homogeneous regions within an image as distinct and belonging to different objects Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. We present herein a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Keywords

Medical Imaging, Classification, Deformable Models, Magnetic Resonance Imaging.

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