In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system. The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification. Fundamental image features of crystals are selected for efficient classification. In particular, a novel shape feature, referred to as inner distance descriptor, is introduced to quantitatively describe different crystal shapes, which is relatively independent of the crystal size and its geometric direction in an image captured for analysis. Moreover, a pixel equivalent calibration method based on subpixel edge detection and circle fitting is proposed to measure crystal sizes from the captured images. In addition, a kernel function based method is given to deal with nonlinear correlations between multiple features of crystals, facilitating computation efficiency for real-time shape identification. Case study and experimental results from the cooling crystallization of l-glutamic acid demonstrate that the proposed image analysis method can be effectively used for in-situ crystal size measurement and shape identification with good accuracy

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Advances in Microfluidics and Lab-on-a-Chip Technologies

Advances in molecular biology are enabling rapid and efficient analyses for effective intervention in domains such as biology research, infectious disease management, food safety, and biodefense. The emergence of microfluidics and nanotechnologies has enabled both new capabilities and instrument sizes practical for point-of-care. It has also introduced new functionality, enhanced sensitivity, and reduced the time and cost involved in conventional molecular diagnostic techniques. This chapter reviews the application of microfluidics for molecular diagnostics methods such as nucleic acid amplification, next-generation sequencing, high resolution melting analysis, cytogenetics, protein detection and analysis, and cell sorting. We also review microfluidic sample preparation platforms applied to molecular diagnostics and targeted to sample-in, answer-out capabilities