SOM-based level set model for image segmentation

In this paper, a novel region based active model integrating self organizing map (SOM) for image segmentation is proposed. The SOM is utilized to describe image intensity distributions in an unsupervised way and establish the entire weights set of the neurons. Then, the weights are divided into two branch sets depending on whether the weight is larger than the weight mean. Further we obtain the means of two weights subsets, which could be regarded as the average intensities of the background and foreground, respectively. In addition, the global intensities updated during contour evolution are replaced by the prototypes of the entire weights set according to certain rule. Along with the two weight means, the global means are averaged to create the threshold for the whole image to guide the contour movement. The proposed model could be successfully applied for image segmentation. Experiment results demonstrate the effectiveness of our model. Introduction Image segmentation has long been thought to be vital for machine learning. Active contour model (ACM) [1-2], which devotes to guiding the contour to move towards the true object boundaries, has been paid more and more attention in image segmentation. It works mainly based on the minimum of the energy function which is constructed by the image statistical information. CV model [1], one of the most famous region-based ACMs, assumes that the image intensities obey Gaussian intensity distributions, and hence approximates the image by two piecewise-constants. Similarly, some of the ACMs employ certain probability model to approximate the intensity distributions [1-2]. And the prior knowledge models are usually applied to simulation environment with great success. However, in certain cases, these models, which are strongly dependent on the statistical assumptions about the image intensity distributions, would limit their practical applications, e.g., in dealing with images that contain noise and weak edges. The model misspecification, in general, is responsible for the dissatisfactory segmentation results. To solve the problem, the CSOM-CV [3-4] combines the advantage of the SOM and ACM and trains two SOMs concurrently: one for the foreground and the other for the background. They replace the region mean intensities by the related prototypes coming from the two trained networks. The SOMs possess the ability of predicting the image intensity distribution using the topology preservation property. Hence, some hybrid ACM models in combination with the SOMs are proposed in the literature to avoid the negative effect of model misspecification. However, there are still some problems, for instance, the foreground and background regions for training are settled in advance. Hence, [5] proposes an unsupervised SOM-based CV model i.e. SOMCV which trains a set of neurons model globally by a self-organization learning procedure. Inspired by the SOMCV and online region-based active contour model i.e. ORACM [6], a novel active model for image segmentation is presented in this paper. The SOM is utilized to describe image intensity distributions in an unsupervised way and establish the entire weights set of the neurons. Then, the weights are divided into two branch sets depending on whether the weight is larger than the weight mean, and thus we obtain the means of two weights subsets which could be regarded as the average intensities of the background and foreground, respectively. In addition, the global intensities updated during contour evolution are replaced by the prototypes of the entire weights set according to certain rule. Along with the two weight means, global means are averaged to create the threshold for the whole image to guide the contour towards the desired boundaries. 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 71