A variational framework for multiregion pairwise-similarity-based image segmentation.

Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques.

Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images.

PURPOSE: To develop an automated tool that provides reliable, consistent, and accurate results for counting cell nuclei in tissue sections. METHODS: We propose a novel method based on an image processing algorithm to analyze large sets of digital micrographs. The nucleus detector design is based on a Laplacian of Gaussian filter. We use the leave-one-out cross validation method for estimating the generalization error, which is then used to choose the model and parameters of the proposed nucleus detector with both fluorescent and dye stained images. We also evaluate the performance of a nucleus detector by comparing the results with manual counts. RESULTS: When our nucleus detector is applied to previously unanalyzed images of feline retina, it correctly counts nuclei within the outer nuclear layer (ONL) with an average error of 3.67% ranging from 0 to 6.07%, and nuclei within the inner nuclear layer (INL) with an average error of 8.55% ranging from 0 to 13.76%. Our approach accurately identifies the location of cell bodies. Even though we have a relatively large error in the INL due to the large intra-observer variation, both manual counting and nucleus detector result in the same conclusion. This is the first time that cell death in the INL in response to retinal detachment is analyzed quantitatively. We also test the proposed tool with various images and show that it is applicable to a wide range of image types with nuclei varying in size and staining intensity. CONCLUSIONS: The proposed method is simple and reliable. It also has widespread applicability to a variety of sample preparation and imaging methods. Our approach will be immediately useful in quantifying cell number in large sets of digital micrographs and from high-throughput imaging. The tool is available as a plug-in for Image J.

Graph partitioning active contours (GPAC) for image segmentation.

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.

A nonconservative flow field for robust variational image segmentation.

We introduce a robust image segmentation method based on a variational formulation using edge flow vectors. We demonstrate the nonconservative nature of this flow field, a feature that helps in a better segmentation of objects with concavities. A multiscale version of this method is developed and is shown to improve the localization of the object boundaries. We compare and contrast the proposed method with well known state-of-the-art methods. Detailed experimental results are provided on both synthetic and natural images that demonstrate that the proposed approach is quite competitive.