arXiv subjects

Li Bai

Publications and source records attributed to Li Bai.

22 records · Page 2Linked to original sources

arXiv2009

Fully Automatic 3D Reconstruction of Histological Images

In this paper, we propose a computational framework for 3D volume reconstruction from 2D histological slices using registration algorithms in feature space. To improve the quality of reconstructed 3D volume, first, intensity variations in images are corrected by an intensity standardization process which maps image intensity scale to a standard scale where similar intensities correspond to similar tissues. Second, a subvolume approach is proposed for 3D reconstruction by dividing standardized slices into groups. Third, in order to improve the quality of the reconstruction process, an automatic best reference slice selection algorithm is developed based on an iterative assessment of image entropy and mean square error of the registration process. Finally, we demonstrate that the choice of the reference slice has a significant impact on registration quality and subsequent 3D reconstruction.

cs.CV↗

arXiv2009

Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition

In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.

cs.CV↗

arXiv2009

Registration of Standardized Histological Images in Feature Space

In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstructed by co-registering 2D slices elastically.

cs.CV↗

arXiv2009

Multiresolution Elastic Medical Image Registration in Standard Intensity Scale

Medical image registration is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local image intensity variations. In this paper we describe a new multiresolution elastic image registration method that challenges these difficulties in image registration. To capture large and small scale image deformations, we use both global and local affine transformation algorithms. To address global and local image intensity variations, we apply an image intensity standardization algorithm to correct image intensity variations. This transforms image intensities into a standard intensity scale, which allows highly accurate registration of medical images.

cs.CV↗