An iterative image segmentation algorithm utilizing spatial information

An iterative image segmentation algorithm that segments an image on a pixel-by-pixel basis is described. The observation information to be utilized is the joint gray level values of the pixel to be segmented and those of its neighborhood pixels. The iterative process is initialized by thresholding the image with Otsu's (1979) method. Each pixel is segmented into a class when the a posteriori probability, conditioned on the observation information, that it belongs to this class is a maximum. The newly segmented image is employed to re-estimate the a posteriori probabilities and the segmentation process is repeated until there is no further pixel classification change in a particular run. Among those segmented images generated in the iterative process, the best segmented image is chosen, according to a maximum entropy criterion. Simulation studies demonstrate that the proposed algorithm can achieve very significant improvement in segmentation performance as compared to the more popular thresholds approach. Furthermore, the performance is neither sensitive to the initial threshold value nor the form of the probability density function of the image. Segmentation of practical images also demonstrates that the proposed algorithm is capable of good segmentation results for real-life images.published_or_final_versio

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation

In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear convolution composed with a nonlinear thresholding; what makes this process special in our case is the presence of additive terms whose role is to "skew" the voting when prior information is available. In real-world implementation, the AM algorithm always converges to a fixed point. We study the behavior of AM rigorously and present a proof of this convergence. The key idea is to formulate AM as a generalized (parallel) majority cellular automaton, adapting proof techniques from discrete dynamical systems