segmentation of mr images using active contours: methods, challenges and applications

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  • International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 02, Volume 4 (February 2017)

    _________________________________________________________________________________________________ IJIRAE: Impact Factor Value SJIF: Innospace, Morocco (2015): 3.361 | PIF: 2.469 | Jour Info: 4.085 |

    Index Copernicus 2014 = 6.57 2014- 17, IJIRAE- All Rights Reserved Page -13

    Segmentation of MR Images using Active Contours:

    Methods, Challenges and Applications

    K V Mahesan* S. Bhargavi D. Jayadevappa Associate Professor Professor, Dept. of ECE Professor, Dept. of E& IE Dept. of Telecommunication SJCIT, Chikkaballapur, India JSSATE, Bengaluru,

    Dr. AIT & Research Scholar VTU, Belagavi, India. Jain University, Bengaluru, India Abstract In recent years, Active contours have been widely studied and applied in medical image analysis. Active contours combine underlying information with high-level prior knowledge to achieve automatic segmentation for complex objects. Their applications include edge detection, segmentation of objects, shape modelling and object boundary tracking. This paper presents the development process of active contour models and describes the classical parametric active contour models, geometric active contour models, and new hybrid active contour models based on curve evolution and energy minimization techniques. It also discusses challenges and applications of active contour models in medical image segmentation.

    Keywords Active Contours, Image Segmentation, Medical Image Analysis.


    Manual tracing of object boundaries generally suffers from poor reproducibility of results and it is also tedious and time consuming. Further, manual segmentations are often restricted to 2D slice-wise processing, often suffer from inconsistency across segmented slices. Quantitative analysis of medical images requires reproducible, accurate and efficient segmentation methods. In this paper, various approaches of medical image segmentation using active contours and available algorithms are reviewed and their advantages, disadvantages and limitations are discussed.

    This paper reviews various types of active contour models used for the segmentation of medical images. Active contour models can be implemented on the continuum and achieve sub pixel accuracy, a highly desirable property for medical imaging applications. Current research on active contour models for medical image segmentation is extensive. Many variations, extension, and alternative formulations appeared since the introduction of traditional snake model [1]. Survey and review articles on active contour models in medical image segmentation are available in the literature [2], [3], [4], [5]. This review is on their basis for comparison of some more medical image segmentation techniques. Active contour models (ACM) are less sensitive to noise, as well as the location of the initial contour, and have better performance with weak boundaries. Hence these models can efficiently detect the exterior and interior tumour boundaries simultaneously.


    Medical image analysis has played a more and more important role in many clinical procedures due to the advancements in medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Active contour models are capable of providing closed and smooth contours or surfaces of target objects with sub-pixel accuracy and have been extensively applied to 2D and 3D image segmentations. These models can be formulated under an energy minimization framework based on the theory of surface evolution and geometric flows. The first model of active contour (Snake model) was proposed in [6] and named snakes due to the appearance of contour evolution and is successfully applied to deal with a wide variety of computer vision applications.

    The snake model is described as a controlled continuity model under the influence of image forces. Internal forces control the bending characteristics of the while image forces, such as the gradient, serve to push the snake toward image features.

  • International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 02, Volume 4 (February 2017)

    _________________________________________________________________________________________________ IJIRAE: Impact Factor Value SJIF: Innospace, Morocco (2015): 3.361 | PIF: 2.469 | Jour Info: 4.085 |

    Index Copernicus 2014 = 6.57 2014- 17, IJIRAE- All Rights Reserved Page -14

    The total energy of the snake is defined as

    1 1 1*

    int0 0 0

    ( ( )) ( ( )) ( ( ))snake snake imageE E s ds E s ds E s ds v v v (1) The splines internal bending energy has been defined by this paper as follows

    2 2

    int ( ) ( ) ( ) ( )E s s s s s ssv v (2)

    The coefficients, and can be used to control the continuity characteristics of the snake by changing its elasticity and rigidity. Application of this basic model is limited because of contour initialization. Berger [7] has proposed the first and primary uses of parametric models in medical image analysis to segment objects in 2D images. However, this classic snake provide an accurate location of the edges only if the initial contour is given sufficiently near the edges, because they make use of only the local information along the contour. This limitation indicates that, basic snake model alone cannot serve the purpose of accurate segmentation and they need further modifications and extensions. Cohen [8] has incorporated an inflation force in the original snake model and the contour curve is treated as a balloon that is inflated in order to avoid local minima solutions i.e., the curve passes over edges and is stopped only if the edge is strong. However, it does not work image with weak edges. Cohen and Cohen [9] used an internal inflation force to expand a snakes model past spurious edges towards the real edges of the structure, making the snake less sensitive to initial conditions. But it suffers with poor capture range. Poon et al. [10] have proposed an algorithm to minimize the energy of active contour models using simulated annealing. This method improves the capture range but noise and other image artefacts can cause incorrect regions or boundary discontinuities in objects recovered by this method.

    A. Gradient Vector Flow and Geodesic Active Contour Models

    In order to overcome the limitations posed by traditional snake, Xu and Prince [11] have made an effort by introducing gradient vector flow as an external force (region based features), that significantly increases the capture range. In this method they replaced the potential force in the traditional equation with a novel external force field called Gradient Vector Flow (GVF). The energy equation is as follows

    2 22 2 2 2x y x yE C u u v v f V f dxdy (3)

    This technique makes the model free from the initial conditions and also they can handle concave objects. But still it poses the following drawbacks. Paragios et al. [12] have introduced a set of diffusion equations applied to image gradient vectors yielding a vector field over the image domain.

    1 ,C g k Vk x k x u v N Nt


    This method has the bidirectional flow and can extract concave object extraction problem, however, it suffers from high computational requirements. To overcome this drawback, Cvancarova et al. [13] proposed several improvements to the original GVF algorithm.

    12 2


    1 ' ''2 ext

    E C s C s E C s ds (5) Traditional snake often converges to local minimum and they do not perform well on noisy images, and their capture range is small. This problem is overcome by Osher and Sethian [14] and suggested some external force model to enhance capture range. By introducing several parameters to the GVF algorithm [15], the capture range is shown to be satisfactory. In order to improve both segmentation quality and computational efficiency, Liu et al. [16] suggested the combination of GVF algorithm and mean shift technique. The improved GVF using mean shift is formulated as,

    11 2 3'' ''' 0g d C s g d C s g d V (6)

    This algorithm reduces the i


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