Raman ChemLighter: Fiber optic Raman probe imaging in combination with augmented chemical reality

Raman spectroscopy using fiber optic probe combines non‐contacted and label‐free molecular fingerprinting with high mechanical flexibility for biomedical, clinical and industrial applications. Inherently, fiber optic Raman probes provide information from a single point only, and the acquisition of images is not straightforward. For many applications, it is highly crucial to determine the molecular distribution and provide imaging information of the sample. Here, we propose an approach for Raman imaging using a handheld fiber optic probe, which is built around computer vision–based assessment of positional information and simultaneous acquisition of spectroscopic information. By combining this implementation with real‐time data processing and analysis, it is possible to create not only fiber‐based Raman imaging but also an augmented chemical reality image of the molecular distribution of the sample surface in real‐time. We experimentally demonstrated that using our approach, it is possible to determine and to distinguish borders of different bimolecular compounds in a short time. Because the method can be transferred to other optical probes and other spectroscopic techniques, it is expected that the implementation will have a large impact for clinical, biomedical and industrial applications.      

Camera-based visibility estimation: Incorporating multiple regions and unlabeled observations

This paper investigates image processing and pattern recognition techniques to estimate atmospheric visibility based on the visual content of images from off-the-shelf cameras. We propose a prediction model that first relates image contrast measured through standard image processing techniques to atmospheric transmission. This is then related to the most common measure of atmospheric visibility, the coefficient of light extinction. The regression model is learned using a training set of images and corresponding light extinction values as measured using a transmissometer.The major contributions of this paper are twofold. First, we propose two predictive models that incorporate multiple scene regions into the estimation: regression trees and multivariate linear regression. Incorporating multiple regions is important since regions at different distances are effective for estimating light extinction under different visibility regimes. The second major contribution is a semi-supervised learning framework, which incorporates unlabeled training samples to improve the learned models. Leveraging unlabeled data for learning is important since in many applications, it is easier to obtain observations than to label them. We evaluate our models using a dataset of images and ground truth light extinction values from a visibility camera system in Phoenix, Arizona.     

Application of novel lossless compression of medical images using prediction and contextual error modeling

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions.     

Biotechnological applications of image analysis: present and future prospects.

The current and potential biotechnological applications of image analysis and image processing systems are reviewed. Image analysis systems have proven to be highly versatile and efficient tools for assisting academic biotechnological research. It is expected that image analysis systems will allow more rapid and accurate quantification of numerous biotechnological analyses. There is, therefore, much scope for the implementation of image analysis/processing systems in a large variety of industrial and clinical applications.     

Objective method of comparing DNA microarray image analysis systems

Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. The image processing system takes pixel-level intensity data and converts it to a set of gene-level expression or copy number summaries that will be used in further analyses. Image analysis systems currently in use differ with regard to the specific algorithms they implement, ease of use, and cost. Thus, it would be desirable to have an objective means of comparing systems. Here we describe a systematic method of comparing image processing results produced by different image analysis systems using a series of replicate microarray experiments. We demonstrate the method with a comparison of cDNA microarray data generated by the UCSF Spot and the GenePix image processing systems.     

A double scanning microphotometer for image analysis: hardware, software and biomedical applications.

A new image processing system designed for densitometry and pattern analysis of microscopic specimens is described with special regard to the hardware, the software and the biologic applications. The data acquisition procedure involves the combination between the scanning of the preparation by means of a motorized stage and the scanning of successive fields by a mechanical device. The signal provided by the photomultiplier is converted into digital values which are directed to an on-line computer. The data processing is based on a one-pass computation involving automata theory and therefore it avoids the storage of the image in the computer memory. In so doing, an entire and continuous image of the whole preparation can be processed at the highest magnification of the microscope whatever the size of the analyzed specimen may be. A biologic application of the system is reported and concerns the automatic identification and counting of cells in the various phases of the mitotic cycle.     

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called 'bioimage informatics'. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.     

Quantitative Imaging with a Mobile Phone Microscope

Use of optical imaging for medical and scientific applications requires accurate quantification of features such as object size, color, and brightness. High pixel density cameras available on modern mobile phones have made photography simple and convenient for consumer applications; however, the camera hardware and software that enables this simplicity can present a barrier to accurate quantification of image data. This issue is exacerbated by automated settings, proprietary image processing algorithms, rapid phone evolution, and the diversity of manufacturers. If mobile phone cameras are to live up to their potential to increase access to healthcare in low-resource settings, limitations of mobile phone–based imaging must be fully understood and addressed with procedures that minimize their effects on image quantification. Here we focus on microscopic optical imaging using a custom mobile phone microscope that is compatible with phones from multiple manufacturers. We demonstrate that quantitative microscopy with micron-scale spatial resolution can be carried out with multiple phones and that image linearity, distortion, and color can be corrected as needed. Using all versions of the iPhone and a selection of Android phones released between 2007 and 2012, we show that phones with greater than 5 MP are capable of nearly diffraction-limited resolution over a broad range of magnifications, including those relevant for single cell imaging. We find that automatic focus, exposure, and color gain standard on mobile phones can degrade image resolution and reduce accuracy of color capture if uncorrected, and we devise procedures to avoid these barriers to quantitative imaging. By accommodating the differences between mobile phone cameras and the scientific cameras, mobile phone microscopes can be reliably used to increase access to quantitative imaging for a variety of medical and scientific applications.     

Bayesian image processing of data from constrained source distributions—II. valued,uncorrelated and correlated constraints

Bayesian image processing formalisms which incorporatea priori information about valued-uncorrelated and valued-correlated (patterned) source distributions are introduced and the corresponding iterative algorithms are derived using the EM technique. Striking improvement in image processing is demonstrated when applying these algorithms to Poisson and Gaussian randomized data in one-dimensional cases.     

Development of an imaging flow cytometer

A cell analyzer that combines the characteristics of image cytometry and flow cytometry is being designed and constructed at the University of Sydney. This paper describes the image acquisition and processing components and some preliminary applications. Cells stained by a fluorescent dye and suspended in a liquid medium are conveyed by a hydraulic system to a flow channel assembly, where they are detected and illuminated by a laser beam. A two-dimensional charge-coupled device is used to acquire the cell images. Image processing and classification is to be carried out by a special-purpose computer comprising an array of four conventional microprocessors and a highly parallel processor consisting of an array of 32 X 32 processing elements. The analyzer will be capable of using morphologic, immunologic and biochemical information to classify and sort up to 500 cells per second. Because of its unique characteristics, the instrument will be of particular use in tumor heterogeneity studies.     

Proteomics in Pathology

Proteomic approaches are of growing importance in the biologist's toolbox. It greatly benefited from past and recent advances in sampling, chemical processing, mass spectrometry (MS) instrumentation, and data processing. MS‐based analysis of proteins is now in the process of being translated in pathology for objective diagnoses. In this viewpoint, we present the workflows that we think are the most promising for applications in pathology. We also comment what we think are prerequisites for a successful translational implementation.     

Classification using sparse representations: a biologically plausible approach

Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.     

昆虫数学形态学研究及其应用展望

数学形态学是用数学方法描述或分析一个物体图象的形状的理论和方法,是图象处理和图象识别技术的发展,但在生物学当中的应用还很有限。本文介绍了一个新的分支学科——昆虫数学形态学,包括三方面的内容：①昆虫数学形态学技术研究,涉及昆虫图象数字化技术和昆虫图象处理与识别技术;②昆虫数学形态学理论研究,主要以昆虫图象的解释和理解研究及昆虫数学形态学与分类学等学科的关系研究为主;③昆虫和昆虫数学形态学应用基础研究,涉及昆虫数学形态学数据库及其分析软件开发,昆虫图象的机器学习和计算机视觉等内容。昆虫数学形态学理论和方法与计算机视觉技术相结合,在害虫虫情监测、昆虫多媒体专家系统的构建等方面具有广阔的应用前景。     

Double-Exposure Optical Sectioning Structured Illumination Microscopy Based on Hilbert Transform Reconstruction

Structured illumination microscopy (SIM) with axially optical sectioning capability has found widespread applications in three-dimensional live cell imaging in recent years, since it combines high sensitivity, short image acquisition time, and high spatial resolution. To obtain one sectioned slice, three raw images with a fixed phase-shift, normally 2π/3, are generally required. In this paper, we report a data processing algorithm based on the one-dimensional Hilbert transform, which needs only two raw images with arbitrary phase-shift for each single slice. The proposed algorithm is different from the previous two-dimensional Hilbert spiral transform algorithm in theory. The presented algorithm has the advantages of simpler data processing procedure, faster computation speed and better reconstructed image quality. The validity of the scheme is verified by imaging biological samples in our developed DMD-based LED-illumination SIM system.     

Registration of pedobarographic image data in the frequency domain

Image registration has been used to support pixel-level data analysis on pedobarographic image data sets. Some registration methods have focused on robustness and sacrificed speed, but a recent approach based on external contours offered both high computational processing speed and high accuracy. However, since contours can be influenced by local perturbations, we sought more global methods. Thus, we propose two new registration methods based on the Fourier transform, cross-correlation and phase correlation which offer high computational speed. We found out that both proposed methods revealed high accuracy for the similarity measures considered, using control geometric transformations. Additionally, both methods revealed high computational processing speed which, combined with their accuracy and robustness, allows their implementation in near-real-time applications. Furthermore, we found that the current methods were robust to moderate levels of noise, and consequently, do not require noise removal procedure like the contours method does.     

Homographic Patch Feature Transform: A Robustness Registration for Gastroscopic Surgery

Image registration is a key component of computer assistance in image guided surgery, and it is a challenging topic in endoscopic environments. In this study, we present a method for image registration named Homographic Patch Feature Transform (HPFT) to match gastroscopic images. HPFT can be used for tracking lesions and augmenting reality applications during gastroscopy. Furthermore, an overall evaluation scheme is proposed to validate the precision, robustness and uniformity of the registration results, which provides a standard for rejection of false matching pairs from corresponding results. Finally, HPFT is applied for processing in vivo gastroscopic data. The experimental results show that HPFT has stable performance in gastroscopic applications.     

Interactive processing and visualization of image data for biomedical and life science applications

Background

Applications in biomedical science and life science produce large data sets using increasingly powerful imaging devices and computer simulations. It is becoming increasingly difficult for scientists to explore and analyze these data using traditional tools. Interactive data processing and visualization tools can support scientists to overcome these limitations.

Results

We show that new data processing tools and visualization systems can be used successfully in biomedical and life science applications. We present an adaptive high-resolution display system suitable for biomedical image data, algorithms for analyzing and visualization protein surfaces and retinal optical coherence tomography data, and visualization tools for 3D gene expression data.

Conclusion

We demonstrated that interactive processing and visualization methods and systems can support scientists in a variety of biomedical and life science application areas concerned with massive data analysis.