Interactive Medical Image Segmentation Using Snake and Multiscale Curve Editing

Wu Zhou,Yaoqin Xie

doi:10.1155/2013/325903

Abstract

Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.

Highlights

Medical image segmentation is of great importance in providing noninvasive information for human body structures that helps clinicians to visualize and study the anatomic structures, track the progress of diseases, and evaluate the need for radiotherapy or surgeries [1]
No segmentation method works well for all the applications, and various approaches have been explored for each computeraided diagnosis (CAD) problem
We explore snake model and curve editing to devise a contour delineation algorithm that consists of manual process and automatic process

Summary

Introduction

Medical image segmentation is of great importance in providing noninvasive information for human body structures that helps clinicians to visualize and study the anatomic structures, track the progress of diseases, and evaluate the need for radiotherapy or surgeries [1]. Even though the research and application of medical images techniques are expanding rapidly, accurate segmentation of medical images meets many challenges in clinical applications due to the inhomogeneity of anatomical structures, low contrast, noise and occlusions. All these challenges make the medical image segmentation difficult in clinical applications. To overcome these challenges, many segmentation methods have been developed and reported in the literature [2]. No segmentation method works well for all the applications, and various approaches have been explored for each computeraided diagnosis (CAD) problem. Segmentation or delineation is still a very active research field and how to design an optimal segmentation approach that fulfills the necessities of clinical applications is extremely essential for medical clinicians

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