The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information. Therefore, a C-RBH-PCANet and matrix-form classifier-based feature learning and classification framework is proposed for diagnosis of color histopathological images. The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C-RBH-PCANet and matrix-form classifier.
Elevated intraocular pressure (IOP) may be the primary risk factor to the development of glaucoma. Finite element (FE) modeling is commonly considered as an effective method to quantitatively analyze pathogenesis of glaucoma. Recent researches focus on establishing partial human eye models. A refined global human eye model was developed using ANSYS software to investigate the correlation between IOP elevation and biomechanical responses. First, the pressure transferring process according to IOP elevation in the whole eye was analyzed to simulate the effects of IOP elevation on glaucoma. Then, the biomechanical responses of the anterior eye segment under various pressure differences between the anterior and posterior chambers (AC and PC) were analyzed to simulate posterior nonadhesion of iris and posterior synechia. This global eye model not only simulated the responses of elevated IOP on ocular structures, but also revealed the process of pressure transferring among each tissue from the anterior eye segment to the optic nerve head (ONH) region. The local mechanical characteristics of the ocular structures obtained from the global model agreed with previous findings. This global model may shed light on the studies of multifactorial glaucoma.
A bistable liquid crystal cell with splay and π twist stable states is obtained by doping a chiral additive in a splay cell filled with dual frequency liquid crystals. The switching between the two states is achieved by using a sequential waveform of low and high frequencies. The switching mechanisms are proposed by using the backflow effect together with the anisotropic properties of dual frequency liquid crystals. As a result, the two stable states have the superior memory characteristics due to the topological inequivalence.
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