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To start the tracking of targets in the environment with heavy clutters is always a problem in radar data procedure. In this paper, a Hough Transform method based on Morphology is proposed to solve the problem of starting track in radar system. This method first uses morphological method to reduce clutter and noise in imagery domain, then use the Hough transform to detect the starting tracks. Simulation results show that the method well detected the short straight lines of target starting track.

A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.

A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized N-dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM) algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.

Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). Recent functional imaging studies have demonstrated regional deactivation magnitudes changes of default-mode network (DMN) in patients, accompanied by a defect of memory function. However, functional connectivity in the DMN during task performance was rarely investigated. METHODS : 26 amnestic MCI and 27 healthy controls underwent functional magnetic resonance imaging (fMRI) during an episodic memory task. Resting-state fMRI data were also collected. The changes in default-mode networks under both task-related and resting-state fMRI were obtained with a temporal correlation analysis. RESULTS: Significantly decreased functional connectivity was found mainly in anterior-posterior connectivity of DMN in aMCI groups. And a correlation existed between the anterior-posterior functional connectivity and MMSE scores. CONCLUSIONS: This study showed that disconnectivity of DMN could be used as an imaging biomarker for predicting the future cognitive decline of aMCI patients.

This article summarizes the progress of the application of functional magnetic resonance image (fMRI) on neuropharmacology. Cholinergic drugs are types of neurotransmitters which are usually used to treat neurological disorders. fMRI is widely used in the research of evaluation of cholinergic drugs. This article systematically summerizes the research of drug evaluation on memory, attention, emotion and vision by fMRI using the cholinergic drugs for example. Combined with the charactors and disciplines of traditional Chinese medicine (TCM), this article also makes a prospect of the application of fMRI on traditional Chinese medicine evaluation, especially the drugs on brain disease and emotional modulation.