Mohammad Reza Daliri

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In this paper we present an automated method for diagnosing Alzheimer disease (AD) from brain MR images. The approach uses the scale-invariant feature transforms (SIFT) extracted from different slices in MR images for both healthy subjects and subjects with Alzheimer disease. These features are then clustered in a group of features which they can be used to(More)
In this paper a kernel method for shape recognition is proposed. The approach is based on the edit distance between pairs of shapes after transforming them into symbol strings. The transformation of shapes into symbol strings is invariant to similarity transforms and can handle partial occlusions. Representation of shape contours uses the shape contexts and(More)
An automatic system for the diagnosis of lung cancer has been proposed in this manuscript. The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM) for the classification of lung cancer data. The dimension of the feature space is reduced by the GA(More)
The central nervous system (CNS) plays an important role in regulation of human gait. Parkinson’s disease (PD) is a common neurodegenerative disease that may cause neurophysiologic change in the CNS and as a result change the gait cycle duration (stride interval). This article used the Hidden Markov Model (HMM) with Gaussian Mixtures to separate the(More)
fects of stroke, trauma or degenerative diseases (such as Alzhiemer’s Disease) on brain function, to monitor the growth and the activity of the brain tumor regions and to plan surgery, radiotherapy or other surgical treatments of the brain. After designing an fMRI paradigm and running the experiment and the data collection, various analysis steps must be(More)
Recently, the multivariate analysis methods have been widely used for predicting the human cognitive states from fMRI data. Here, we explore the possibility of predicting the human cognitive states using a pattern of brain activities associated with thinking about concrete objects. The fMRI signals in conjunction with pattern recognition methods were used(More)
In this paper we present two algorithms for shape recognition. Both algorithms map the contour of the shape to be recognized into a string of symbols. The first algorithm is based on supervised learning using string kernels as often used for text categorization and classification. The second algorithm is very weakly supervised and is based on the procrustes(More)