Method For Simultaneous Segmentation Research Articles

Simultaneous band selection and segmentation of hyperspectral images via a mixture of finite maximum margin mixtures

ABSTRACT Unsupervised segmentation of hyperspectral images is an important problem in the field of image processing with many applications, including scene labelling of remote-sensing images and object detection in medical images. However, the high-dimensionality of hyperspectral images poses considerable challenges in this problem. In previous studies, to deal with high-dimensionality, band selection has been performed as a separate step before image segmentation. In this approach, however, information loss can occur because band selection methods do not consider the segmentation task when they select the bands. In addition, in most segmentation tasks, the fact that the number of segments of the image needs to be given before the segmentation task is also an issue. To address these challenges, we propose a method for simultaneous segmentation of hyperspectral images and selection of important bands that are highly involved in the segmentation task. The proposed method, called a mixture of finite maximum margin mixtures, combines a mixture of finite mixtures, which allows the number of segments to be automatically inferred from the data, and a maximum margin classifier, which allows a flexible segmentation and tractable inference on the segmentation labels. Furthermore, we simultaneously select useful bands that help with the segmentation during the segmentation procedure by introducing a latent binary vector that represents discriminating or non-discriminating bands. We demonstrate the advantages of the proposed method using four real datasets.

Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms

In this work, we present a new methodology to simultaneously segment anatomical structures in medical images. Additionally, this methodology is instantiated in a method that is used to simultaneously segment the optic disc (OD) and fovea in retinal images. The OD and fovea are important anatomical structures that must be previously identified in any image-based computer-aided diagnosis system dedicated to diagnosing retinal pathologies that cause vision problems. Basically, the simultaneous segmentation method uses an OD-fovea model and an evolutionary algorithm. On the one hand, the model is built using the intra-structure relational knowledge, associated with each structure, and the inter-structure relational knowledge existing between both and other retinal structures. On the other hand, the evolutionary algorithm (differential evolution) allows us to automatically adjust the instance parameters that best approximate the OD-fovea model in a given retinal image. The method is evaluated in the MESSIDOR public database. Compared with other recent segmentation methods in the related literature, competitive segmentation results are achieved. In particular, a sensitivity and specificity of 0.9072 and 0.9995 are respectively obtained for the OD. Considering a success when the distance between the detected and actual center is less than or equal to $$\eta$$ times the OD radius, the success rates obtained for the fovea are 97.3% and 99.0% for $$\eta =1/2$$ and $$\eta =1$$ , respectively. The segmentation average time per image is 29.35 s.

Segmentation of bias field induced brain MR images using rough sets and stomped-t distribution

Over the past few decades, automatic segmentation of brain magnetic resonance (MR) images into different tissue classes has remained an important research area, particularly due to the presence of bias field artifact in MR images. In this regard, the stomped normal (StN) distribution is proved to generate an optimal representation of the intensity distribution in brain MR images, by incorporating the properties of rough sets in the probabilistic framework. The StN distribution is capable of successfully modelling the central tendency, dispersion, and width of the intensity distribution. However, it does not take into consideration the kurtosis of the distribution, which controls the concentration of values around the mean and shape of the tail of intensity distribution. In this regard, the paper presents a novel method for simultaneous segmentation and bias field correction in brain MR images. It integrates the concept of rough sets and the merit of a recently introduced probability distribution, called stomped-t (St-t) distribution. The St-t distribution incorporates the property of kurtosis in rough-probabilistic framework, where each tissue class is modelled using a crisp lower approximation and a probabilistic boundary region. The brain MR image is modelled using a mixture of finite number of St-t distributions and one uniform distribution. The uniform distribution takes into account cerebro-spinal fluid, pathologies, and other non-brain tissues. The proposed method employs both expectation-maximization algorithm and hidden Markov random field model for accurate and robust image segmentation. The performance of the proposed approach, along with a comparison with related methods, is illustrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

Developing a Procedure for Segmenting Meshed Heat Networks of Heat Supply Systems without Outflows

The heat supply systems of cities have, as a rule, a ring structure with the possibility of redistributing the flows. Despite the fact that a ring structure is more reliable than a radial one, the operators of heat networks prefer to use them in normal modes according to the scheme without overflows of the heat carrier between the heat mains. With such a scheme, it is easier to adjust the networks and to detect and locate faults in them. The article proposes a formulation of the heat network segmenting problem. The problem is set in terms of optimization with the heat supply system’s excessive hydraulic power used as the optimization criterion. The heat supply system computer model has a hierarchically interconnected multilevel structure. Since iterative calculations are only carried out for the level of trunk heat networks, decomposing the entire system into levels allows the dimensionality of the solved subproblems to be reduced by an order of magnitude. An attempt to solve the problem by fully enumerating possible segmentation versions does not seem to be feasible for systems of really existing sizes. The article suggests a procedure for searching rational segmentation of heat supply networks with limiting the search to versions of dividing the system into segments near the flow convergence nodes with subsequent refining of the solution. The refinement is performed in two stages according to the total excess hydraulic power criterion. At the first stage, the loads are redistributed among the sources. After that, the heat networks are divided into independent fragments, and the possibility of increasing the excess hydraulic power in the obtained fragments is checked by shifting the division places inside a fragment. The proposed procedure has been approbated taking as an example a municipal heat supply system involving six heat mains fed from a common source, 24 loops within the feeding mains plane, and more than 5000 consumers. Application of the proposed segmentation procedure made it possible to find a version with required hydraulic power in the heat supply system on 3% less than the one found using the simultaneous segmentation method.