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While the existing quaternion principal component analysis (QPCA) is a linear tool developed mainly for processing linear quaternion signals, the quaternion representation (QR) used in QPCA creates redundancy when representing a color image signal of three components by a quaternion matrix having four components. In this paper, the kernel technique is used to improve the QPCA as kernel QPCA (KQPCA) for processing nonlinear quaternion signals; in addition, both RGB information and depth information are considered to improve QR for representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. We first provide the basic idea of three types of our KQPCA and then propose an algorithm for RGB-D object recognition based on bidirectional two-dimensional KQPCA (BD2DKQPCA) and the improved QR. Experimental results on four public datasets demonstrate that the proposed BD2DKQPCA-based algorithm achieves the best performance among seventeen compared algorithms including other existing PCA-based algorithms, irrespective of RGB object recognition or RGB-D object recognition. Moreover, for all compared algorithms, consideration of both RGB and depth information is shown to achieve better performance in object recognition than considering only RGB information. [ABSTRACT FROM AUTHOR]

Accurate segmentation for magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of intensity inhomogeneity (also named as bias field) and noise in the MR images, many segmentation methods are suffer from limited robustness and hard to find accurate results. This paper proposes an improved anisotropic multivariate student t-distribution based hierarchical fuzzy c-means method (IAMTHFCM). Firstly, improved anisotropic spatial information, defined in the neighborhoods of each pixel, is proposed to overcome the effect of the noise and preserve more detail information, especially for points with less repetitive patterns, such as corner and end points. Secondly, the improved anisotropic spatially information is utilized into a negative multivariate student t-distribution based log-posterior as the dissimilarity function to improve the robustness and accuracy. Thirdly, we use the hierarchical strategy to construct a more flexible objective function by considering the improved dissimilarity function itself as a sub-FCM, to make the method more robust and accurate to outliers and weak edges. Finally, the intensity inhomogeneities is modeled as a linear combination of a set of orthogonal basis functions, and parameterized by the coefficients of the orthogonal basis functions. Then the objective function is integrated with the bias field estimation and makes the proposed method can estimate the bias field meanwhile segmenting images. The segmentation and the bias field estimation can obtain benefit from each other. Our statistical results on both synthetic and clinical images show that the proposed method can overcome the difficulties caused by noise and bias fields and obtain more accurate results. [ABSTRACT FROM AUTHOR]

Rapid, sensitive and accurate point-of-care-testing (POCT) of bacterial load from a variety of samples can help prevent human infections caused by pathogenic bacteria and mitigate their spreading. However, there is an unmet demand for a POCT device that can detect extremely low concentrations of bacteria in raw samples. Herein, we introduce the 'count-on-a-cartridge' (COC) platform for quantitation of the food-borne pathogenic bacteria Staphylococcus aureus. The system comprised of magnetic concentrator, sensing cartridge and fluorescent image reader with a built-in counting algorithm facilitated fluorescent microscopic bacterial enumeration in user-convenient manner with high sensitivity and accuracy within a couple of hours. The analytical performance of this assay is comparable to that of a standard plate count. The COC assay shows a sensitivity of 92.9% and specificity of 100% performed according to global microbiological criteria for S. aureus which is acceptable below 100 CFU/g in the food matrix. This culture-independent, rapid, ultrasensitive and highly accurate COC assay has great potential for places where prompt bacteria surveillance is in high demand. • A fully integrated bacterial detection system based on 'count-on-a-cartridge (COC)' platform is devloped. • Culture-free, rapid, accurate assay protocol within a couple of hours is established with immunomagnetic concentration. • The COC platform has user-convenience without intensive labor and instruments in the lab. • Statistical accuracy of the system for S. aureus spiked in food samples is confirmed comparable to plate count method. [ABSTRACT FROM AUTHOR]