Wamiq Manzoor Ahmed

Classification of Bacterial Contamination Using Image Processing and Distributed Computing

Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In this study, we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine classifier with a linear kernel was used. With extensive testing, we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.

State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Rapid advances in optical instrumentation, high-speed cameras, and fluorescent probes have spurred tremendous growth in the volume of biomolecular imaging data. Various optical imaging modalities are used for probing biological systems in vivo and in vitro. These include traditional two-dimensional imaging, three-dimensional confocal imaging, time-lapse imaging, and multispectral imaging. Many applications require a combination of these imaging modalities, which gives rise to huge data sets. However, lack of powerful information extraction and quantitative analysis tools poses a major hindrance to exploiting the full potential of the information content of these data. In particular, automated extraction of semantic information from multimodality imaging data, crucial for understanding biological processes, poses unique challenges. Information extraction from large sets of biomolecular imaging data requires modeling at multiple levels of detail to allow not only quantitative analysis but also interpretation and extraction of high-level semantic information. In this paper, we survey the state of the art in the area of information extraction and automated analysis tools for in vivo and in vitro biomolecular imaging. The modeling and knowledge extraction for these data require sophisticated image processing and machine learning techniques, as well as formalisms for information extraction and knowledge management. Development of such tools has the potential to significantly improve biological discovery and drug development processes.

SEMANTIC ANALYSIS OF BIOLOGICAL IMAGING DATA: CHALLENGES AND OPPORTUNITIES

Microscopic imaging is one of the most common techniques for investigating biological systems. In recent years there has been a tremendous growth in the volume of biological imaging data owing to rapid advances in optical instrumentation, high-speed cameras and fluorescent probes. Powerful semantic analysis tools are required to exploit the full potential of the information content of these data. Semantic analysis of multi-modality imaging data, however, poses unique challenges. In this paper we outline the state-of-the-art in this area along with the challenges facing this domain. Information extraction from biological imaging data requires modeling at multiple levels of detail. While some applications require only quantitative analysis at the level of cells and subcellular objects, others require modeling of spatial and temporal changes associated with dynamic biological processes. Modeling of biological data at different levels of detail allows not only quantitative analysis but also the extraction of high-level semantics. Development of powerful image interpretation and semantic analysis tools has the potential to significantly help in understanding biological processes, which in turn will result in improvements in drug development and healthcare.

Quantitative Analysis of Inter-object Spatial Relationships in Biological Images

Study of spatial relations between biological objects is crucial for understanding biological processes. Monitoring drug or particle delivery inside cells and studying the dynamics of subcellular proteins are some of the examples. Biological applications have varying demands in terms of speed and accuracy. While accuracy may be the most important factor for small-scale biology, speed is also a concern for high-content/high-throughput screening applications. In this paper we present a variety of algorithms for inter-object spatial relations in two-and three-dimensional space. These algorithms provide trade-off between speed and accuracy, depending on the requirements of the application. Results for speed and accuracy are reported for real as well as synthetic data sets.

Knowledge Extraction for High-Throughput Biological Imaging

We present a multilayered architecture and spatiotemporal models for searching, retrieving, and analyzing high-throughput biological imaging data. The analysis is divided into low-and high-level processing. At the lower level, we address issues like segmentation, tracking, and object recognition, and at the high level, we use finite state machine-and Petri-net-based models for spatiotemporal event recognition.

Knowledge representation and data mining for biological imaging.

Biological and pharmaceutical research relies heavily on microscopically imaging cell populations for understanding their structure and function. Much work has been done on automated analysis of biological images, but image analysis tools are generally focused only on extracting quantitative information for validating a particular hypothesis. Images contain much more information than is normally required for testing individual hypotheses. The lack of symbolic knowledge representation schemes for representing semantic image information and the absence of knowledge mining tools are the biggest obstacles in utilizing the full information content of these images. In this paper we first present a graph-based scheme for integrated representation of semantic biological knowledge contained in cellular images acquired in spatial, spectral, and temporal dimensions. We then present a spatio-temporal knowledge mining framework for extracting non-trivial and previously unknown association rules from image data sets. This mechanism can change the role of biological imaging from a tool used to validate hypotheses to one used for automatically generating new hypotheses. Results for an apoptosis screen are also presented.

Rapid Detection and Classification of Bacterial Contamination Using Grid Computing

Bacterial contamination of food products is a serious public health problem that creates high costs for the food-processing industry. Rapid detection of bacterial pathogens is the key to avoiding disease outbreaks and costly product recalls associated with food-borne pathogens. Automated identification of pathogens using scatter patterns of bacterial colonies is a promising technique that uses image processing and machine learning approaches to extract features from forward-scatter patterns produced by irradiating bacterial colonies with red laser light. The feature vector used for this approach can consist of hundreds of features, and a sufficiently large number of training images is required for accurate classification. As most feature extraction algorithms have high computational cost, the feature extraction step becomes the bottleneck in the whole processing pipeline. Computational grid technologies provide a promising and economical solution to this problem. In this work we report the implementation of the laser-scatter-analysis technique on a computational grid. A set of more than 2000 images was used for training of classifiers. The invariant form of Zernike moments up to order 20, radial Chebyshev moments, and Haralick features were extracted. Linear discriminant analysis and support vector machine classifiers were used for classification. We report speed-ups achieved and the scalability of this approach for large sets of images and for higher-order moments. Laser-scatter-analysis technique combined with computational grid technology offers a feasible and economic solution for rapid and accurate detection and classification of bacterial contamination.

Multispectral imaging analysis: spectral deconvolution and applications in biology

Multispectral imaging has been in use for over half a century. Owing to advances in digital photographic technology, multispectral imaging is now used in settings ranging from clinical medicine to industrial quality control. Our efforts focus on the use of multispectral imaging coupled with spectral deconvolution for measurement of endogenous tissue fluorophores and for animal tissue analysis by multispectral fluorescence, absorbance, and reflectance data. Multispectral reflectance and fluorescence images may be useful in evaluation of pathology in histological samples. For example, current hematoxylin/eosin diagnosis limits spectral analysis to shades of red and blue/grey. It is possible to extract much more information using multispectral techniques. To collect this information, a series of filters or a device such as an acousto-optical tunable filter (AOTF) or liquid-crystal filter (LCF) can be used with a CCD camera, enabling collection of images at many more wavelengths than is possible with a simple filter wheel. In multispectral data processing the “unmixing” of reflectance or fluorescence data and analysis and the classification based upon these spectra is required for any classification. In addition to multispectral techniques, extraction of topological information may be possible by reflectance deconvolution or multiple-angle imaging, which could aid in accurate diagnosis of skin lesions or isolation of specific biological components in tissue. The goal of these studies is to develop spectral signatures that will provide us with specific and verifiable tissue structure/function information. In addition, relatively complex classification techniques must be developed so that the data are of use to the end user.

TeleProbe: design and development of an efficient system for telepathology

This paper describes an internet-based system for telepathology. This system provides support for multiple users and exploits the opportunities for optimization that arise in multi-user environment. Techniques for increasing system responsiveness by improving resource utilization and lowering network traffic are explored. Some of the proposed optimizations include an auto-focus module, client and server side caching, and request reordering. These systems can be an economic solution not only for remote pathology consultation but also for pathology and biology education.