For many medical segmentation tasks, the contrast along most of the boundary of the target object is high, allowing simple thresholding or region growing approaches to provide nearly sufficient solutions for the task. However, the regions recovered by these techniques frequently leak through bottlenecks in which the contrast is low or nonexistent. We propose a new approach based on a novel speed-up of the isoperimetric algorithm [1] that can solve the problem of leaks through a bottleneck. The speed enhancement converts the isoperimetric segmentation algorithm to a fast, linear-time computation by using a tree representation as the underlying graph instead of a standard lattice structure. In this paper, we show how to create an appropriate tree substrate for the segmentation problem and how to use this structure to perform a lineartime computation of the isoperimetric algorithm. This approach is shown to overcome common problems with watershed-based techniques and to provide fast, high-quality results on large datasets.

Abstract—The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique. Index Terms—Geodesic active contours (GACs), iriscodes, iris recognition, iris segmentation, level sets, snakes. Fig. 1. Block diagram of an iris recognition system.

Figure 1: In the left three images, an interactive segmentation of a brain tumor with an active surface model given in implicit form is evolving toward the final segmentation using the level set method [Lefohn et al. 2003]. In the right image, the same method is applied to segmentation of a mouse liver. Level set computations and simultaneous visualization are performed entirely on the GPU (graphics processing unit), which performs general floating-point computations in addition to rendering. Recent advances in the computational power of graphics processing units (GPUs) have turned them into a viable platform for general purpose floating-point computations. A very promising application of these new capabilities is interactive segmentation of medical volume data, which usually involves solving a large number of partial differential equations (PDEs) for each iteration of an evolving segmentation that can be viewed and guided by user input while it is being calculated, and is thus computationally very demanding. We give an overview of segmentation algorithms with a focus on leveraging the power of GPUs in order to obtain high-quality segmentations of medical data in an interactive process, with the premise that these algorithms will lead to faster and higher-quality segmentations in clinical practice in the near future. 2

In material science and engineering, the grain structure inside a super-alloy sample determines its mechanical and physical properties. In this paper, we develop a new Multichannel Edge-Weighted Centroidal Voronoi Tessellation (MCEWCVT) algorithm to automatically segment all the 3D grains from microscopic images of a super-alloy sample. Built upon the classical k-means/CVT algorithm, the proposed algorithm considers both the voxel-intensity similarity within each cluster and the compactness of each cluster. In addition, the same slice of a super-alloy sample can produce multiple images with different grain appearances using different settings of the microscope. We call this multichannel imaging and in this paper, we further adapt the proposed segmentation algorithm to handle such multichannel images to achieve higher grain-segmentation accuracy. We test the proposed MCEWCVT algorithm on a 4-channel Nibased 3D super-alloy image consisting of 170 slices. The segmentation performance is evaluated against the manually annotated ground-truth segmentation and quantitatively compared with other six image segmentation/edgedetection methods. The experimental results demonstrate the higher accuracy of the proposed algorithm than the comparison methods. 1.