BackgroundClinical diagnosis and therapy for the lumbar disc herniation requires accurate vertebra segmentation. The complex anatomical structure and the degenerative deformations of the vertebrae makes its segmentation challenging.MethodsAn improved level set method, namely edge- and region-based level set method (ERBLS), is proposed for vertebra CT images segmentation. By considering the gradient information and local region characteristics of images, the proposed model can efficiently segment images with intensity inhomogeneity and blurry or discontinuous boundaries. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a simple initialization method for the level set function is built, which utilizes the Otsu threshold. In addition, the need of the costly re-initialization procedure is completely eliminated.ResultsExperimental results on both synthetic and real images demonstrated that the proposed ERBLS model is very robust and efficient. Compared with the well-known local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. The proposed method has also applied to 56 patient data sets and produced very promising results.ConclusionsAn improved level set method suitable for vertebra CT images segmentation is proposed. It has the flexibility of segmenting the vertebra CT images with blurry or discontinuous edges, internal inhomogeneity and no need of re-initialization.
Instar determination is fundamental to both basic entomological research and its application. The cockroach, Blaptica dubia Serville (Blattodea: Blaberidae), is a popular pet and an excellent feeder insect for many reptiles and amphibians. A new method using Caussian mixture models to determine the number of instars in this species is developed. Application ofthe method is illustrated by analysis of data collected on B. dubia. The analysis indicates that there are seven instars in B. dubia and that the growth ratio follows the Brooks-Dyar rule. The growth ratio of pronotal length, pronotal width, and head width are 1.26,1.24, and 1.19, respectively. Because B. dubia shares a similar growth pattern with other paurometabolous insects, this method may be applicable to other species as well.
In this paper, we propose an active contour model based on nonparametric independent and identically distributed (i.i.d.) statistics of the image that can segment an image without any a priori information about the intensity distributions of the region of interest or the background. This is not, however, the first active contour model proposed to solve the segmentation problem under these same assumptions. In contrast to prior active contour models based on nonparametric i.i.d. statistics, we do not formulate our optimization criterion according to any distance measure between estimated probability densities inside and outside the active contour. Instead, treating the segmentation problem as a pixel-wise classification problem, we formulate an active contour to minimize the unbiased pixel-wise average misclassification probability (AMP). This not only simplifies the problem by avoiding the need to arbitrarily select among many sensible distance measures to measure the difference between the probability densities estimated inside and outside the active contour, but it also solves a numerical conditioning problem that arises with such prior active contour models. As a result, the AMP model exhibits faster convergence with higher accuracy and robustness when compared to active contour models previously formulated to solve the same nonparametric i.i.d. statistical segmentation problem via probability distances. To discuss this improved numerical behavior more precisely, we introduce the notion of "conditioning ratio" and demonstrate that the proposed AMP active contour is numerically better conditioned (i.e., exhibits a much smaller conditioning ratio) than prior probability distance-based active contours.
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