In this paper, Kernel Fuzzy C-Means (KFCM) algorithm was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. First, the KFCM algorithm computes the fuzzy membership values for each pixel. On the basis of the KFCM, the edge indicator function was redefined. Using the edge indicator function, the biometric fingerprint segmentation of a medical image was performed to extract the regions of interest for advanced processing. In this process, the complexity of time iteration is reduced compared to the Fuzzy C-Means (FCM) algorithm. The above process of segmentation showed a considerable improvement in the evolution of the level set function.

Image segmentation plays an important role in the field of image understanding, image analysis and pattern identification. The foremost goal of the segmentation process is to partition an image into regions that are homogeneous (uniform) with respect to one or more self-characteristic and feature. Clustering has long been a popular approach to untested pattern recognition. The Fuzzy C-Means (FCM) algorithm (Bezdek, 1981), as a typical clustering algorithm, has been utilized in a wide range of engineering and scientific disciplines such as medicine imaging, bioinformatics, pattern recognition and data mining.

The level set method is based on a geometric deformable model, which translates the problem of evolution of 2-D (3-D) close curve (surface) into the evolution of level set function in the space with a higher dimension to obtain the advantage in managing the topology change of the shape (Osher and Sethian, 1988; Malladi et al., 1995; Leventon et al., 2000; and Staib et al., 2000). The level set method has had great success in computer graphics and vision. Also, it has been widely used in medical imaging for segmentation and shape recovery (Paragios and Deriche, 2000; and Vese and Chan, 2002).

However, there are some insufficiencies in the traditional level set method. Firstly, as the local marginal information of the image is used, it is difficult to obtain a perfect result when there is a fuzzy or discrete boundary in the region, and the leaking problem inescapably appears. Secondly, solving the partial differential equation of the level set function requires numerical processing at each point of the image domain, which is a time-consuming process. Finally, if the initial evolution contour is given at will, the iteration time would increase greatly—too large or too small contour will cause the convergence of evolution curve to the contour of object incorrectly. Therefore, some modification has been proposed to improve the speed function of curve evolution (Sethian, 1999; Li et al., 2005; and Shi and Karl, 2005).

Electrical and Electronics Engineering Journal, Biometric Fingerprint Segmentation, Kernel Fuzzy C-Means Algorithm, Image Segmentation, Data Mining, Gaussian Kernel Function, Reinitialization Process, Medical Images, Medical Imaging Characteristics.