Medical image segmentation plays an important role in medical-imaging applications and they provide a large amount of functional and anatomical information, which improve and facilitate diagnosis and disease therapy planning. However, the existence of image artifacts, such as intensity inhomogeneity, noise and partial volume in magnetic resonance images (MRIs), can adversely affect the quantitative image analysis. There are different segmentation methods in the literature, which segment brain MRI into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). However, there is not a common algorithm that can be used for all types of images. We present a critical appraisal of the current status of techniques for MRI segmentation. In this paper, commonly used segmentation algorithms are reviewed and summarized with an emphasis on their characteristics, advantages and disadvantages of these techniques. These are categorized into five different groups based on their workflows and segmentation principles. Different solutions are also proposed to compensate the existing problems in each algorithm. This paper also addresses the issue of quantitative evaluation of segmentation results.

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Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using [Formula: see text]-gram models unigram, bigram and the combination of unigram and bigram as feature extraction with traditional feature weighting methods (binary, term frequency (TF), and term frequency-document frequency (TF-IDF)), while document frequency (DF) was used in order to generate feature spaces with different dimensions to evaluate [Formula: see text]-gram models and traditional feature weighting methods. We performed some experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of Linear and Gaussian radial basis function (RBF). We concluded that feature selection and feature weighting methods can have a substantial role in sentiment classification. Furthermore, the results showed that the proposed work which combined unigram and bigram along with TF-IDF feature weighting method and optimized RBF kernel SVM produced high classification accuracy in financial news classification.

BackgroundBrain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex).MethodsIn this paper we present a simple and more accurate automated technique for brain segmentation into White Matter, Gray Matter and Cerebrospinal fluid (CSF) in three-dimensional MR images. The algorithm’s three steps are histogram based segmentation, feature extraction and final classification using SVM. The integrated algorithm has more accurate results than what can be obtained with its individual components. To produce much more efficient segmentation method our framework captures different types of features in each step that are of special importance for MRI, i.e., distributions of tissue intensities, textural features, and relationship with neighboring voxels or spatial features.ResultsOur method has been validated on real images and simulated data, with desirable performance in the presence of noise and intensity inhomogeneities.ConclusionsThe experimental results demonstrate that our proposed method is a simple and accurate technique to define brain tissues with high reproducibility in comparison with other techniques.Virtual SlidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_207