For classifying Alzheimer's disease (AD) by analyzing medical image data, in this paper a computer-aided diagnosis method is proposed based on random forest algorithm. In this study functional magnetic resonance imaging (fMRI) data including 34 AD patients, 35 mild cognitive impairments (MCI) and 35 normal controls (NC) is collected. Firstly, functional connection between the different regions of whole brain is calculated using Pearson correlation coefficient. Then the importance of the functional connection between different brain regions is measured and the important features are selected using the random forest algorithm. Finally, classification is performed using support vector machine (SVM) classifier with ten-fold cross-validation. The classification model based on random forest and SVM has a good effect on the recognition of AD, and the classification accuracy rate can reach 90.68%. Functional connection characteristics can be effectively analyzed by the random forest algorithm which can distinguish AD, MCI and NC accurately. At the same time, the abnormal brain regions of AD pathogenesis can be obtained. The related experimental results can provide an objective reference for the early clinical diagnosis of AD.
Fluorescence microscopic image three-dimensional (3-D) reconstruction is a challenging topic in image processing and computer vision, and can be widely applied to life science, biology, and medicine. A microscopic images 3-D reconstruction method is proposed for transparent or partially transparent microscopic samples, which is based on the Taylor expansion theorem and polynomial fitting. First, the image stack of the specimen is divided into several groups in an overlapping or nonoverlapping way along the optical axis, and the first image of every group is regarded as the reference image. Then, different order intensity derivatives are calculated using all the images of every group and a polynomial fitting method. Subsequently, a new image can be generated by means of Taylor expansion theorem and the calculated different order intensity derivatives and for which the distance to the reference image is Δz along the optical axis. Finally, the microscopic specimen can be reconstructed in 3-D form using deconvolution technology and all the images including both the observed and the generated images. The experimental results show the superior performance via processing simulated and real fluorescence microscopic degraded images.
Microscopic image restoration and reconstruction is a challenging topic in the image processing and computer vision,
which can be widely applied to life science, biology and medicine etc. A microscopic light field creating and three
dimensional (3D) reconstruction method is proposed for transparent or partially transparent microscopic samples, which
is based on the Taylor expansion theorem and polynomial fitting. Firstly the image stack of the specimen is divided into
several groups in an overlapping or non-overlapping way along the optical axis, and the first image of every group is
regarded as reference image. Then different order intensity derivatives are calculated using all the images of every group
and polynomial fitting method based on the assumption that the structure of the specimen contained by the image stack
in a small range along the optical axis are possessed of smooth and linear property. Subsequently, new images located
any position from which to reference image the distance is Δz along the optical axis can be generated by means of
Taylor expansion theorem and the calculated different order intensity derivatives. Finally, the microscopic specimen can
be reconstructed in 3D form using deconvolution technology and all the images including both the observed images and
the generated images. The experimental results show the effectiveness and feasibility of our method.
A personal identification method is proposed which uses face and ear together to overcome mass information loss resulting from pose changes. Several aspects are mainly considered: First, ears are at both sides of the face. Their physiological position is approximately orthogonal and their information is complementary to each other when the head pose changes. Therefore, fusing the face and ear is reasonable. Second, the texture feature is extracted using a uniform local binary pattern (ULBP) descriptor which is more compact. Third, Haar wavelet transform, blocked-based, and multiscale ideas are taken into account to further strengthen the extracted texture information. Finally, texture features of face and ear are fused using serial strategy, parallel strategy, and kernel canonical correlation analysis to further increase the recognition rate. Experimental results show that it is both fast and robust to use ULBP to extract texture features. Haar wavelet transform, block-based, and multiscale methods can effectively enhance texture information of the face or ear ULBP descriptor. Multimodal biometrics fusion about face and ear is feasible and effective. The recognition rates of the proposed approach outperform remarkably those of the classic principal component analysis (PCA), kernel PCA, or Gabor texture feature extraction method especially when sharp pose change
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