De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

Automated species recognition in environmental recordings

Faunal vocalisations are vital indicators for environmental change and faunal vocalisation analysis can provide information for answering ecological questions. Therefore, automated species recognition in environmental recordings has become a critical research area. This thesis presents an automated species recognition approach named Timed and Probabilistic Automata. A small lexicon for describing animal calls is defined, six algorithms for acoustic component detection are developed, and a series of species recognisers are built and evaluated.The presented automated species recognition approach yields significant improvement on the analysis performance over a real world dataset, and may be transferred to commercial software in the future.

Automated counting of the Northern Pacific Sea Star in the Derwent using shape recognition

A novel shape recognition algorithm was developed to autonomously classify the Northern Pacific Sea Star (Asterias amurenis) from benthic images that were collected by the Starbug AUV during 6km of transects in the Derwent estuary. Despite the effects of scattering, attenuation, soft focus and motion blur within the underwater images, an optimal joint classification rate of 77.5% and misclassification rate of 13.5% was achieved. The performance of algorithm was largely attributed to its ability to recognise locally deformed sea star shapes that were created during the segmentation of the distorted images.

A pattern adaptive thinning algorithm

A simple sequential thinning algorithm for peeling off pixels along contours is described. An adaptive algorithm obtained by incorporating shape adaptivity into this sequential process is also given. The distortions in the skeleton at the right-angle and acute-angle corners are minimized in the adaptive algorithm. The asymmetry of the skeleton, which is a characteristic of sequential algorithm, and is due to the presence of T-corners in some of the even-thickness pattern is eliminated. The performance (in terms of time requirements and shape preservation) is compared with that of a modern thinning algorithm.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Entropy based Skew correction of document images

The document images that are fed into an Optical Character Recognition system, might be skewed. This could be due to improper feeding of the document into the scanner or may be due to a faulty scanner. In this paper, we propose a skew detection and correction method for document images. We make use of the inherent randomness in the Horizontal Projection profiles of a text block image, as the skew of the image varies. The proposed algorithm has proved to be very robust and time efficient. The entire process takes less than a second on a 2.4 GHz Pentium IV PC.

Innate Immune Recognition of RNA-virus infection in human macrophages

Innate immunity and host defence are rapidly evoked by structurally invariant molecular motifs common to microbial world, called pathogen associated molecular patterns (PAMPs). In addition to PAMPs, endogenous molecules released in response to inflammation and tissue damage, danger associated molecular patterns (DAMPs), are required for eliciting the response. The most important PAMPs of viruses are viral nucleic acids, their genome or its replication intermediates, whereas the identity and characteristics of virus infection-induced DAMPs are poorly defined. PAMPs and DAMPs engage a limited set of germ-line encoded pattern recognition receptors (PRRs) in immune and non-immune cells. Membrane-bound Toll-like receptors (TLRs), cytoplasmic retinoic acid inducible gene-I (RIG-I)-like receptors (RLRs) and nucleotide-binding oligomerization domain-like receptor (NLRs) are important PRRs involved in the recognition of the molecular signatures of viral infection, such as double-stranded ribonucleic acids (dsRNAs). Engagement of PRRs results in local and systemic innate immune responses which, when activated against viruses, evoke secretion of antiviral and pro-inflammatory cytokines, and programmed cell death i.e., apoptosis of the virus-infected cell. Macrophages are the central effector cells of innate immunity. They produce significant amounts of antiviral cytokines, called interferons (IFNs), and pro-inflammatory cytokines, such as interleukin (IL)-1β and IL-18. IL-1β and IL-18 are synthesized as inactive precursors, pro-IL-1β and pro-IL-18, that are processed by caspase-1 in a cytoplasmic multiprotein complex, called the inflammasome. After processing, these cytokines are biologically active and will be secreted. The signals and secretory routes that activate inflammasomes and the secretion of IL-1β and IL-18 during virus infections are poorly characterized. The main goal of this thesis was to characterize influenza A virus-induced innate immune responses and host-virus interactions in human primary macrophages during an infection. Methodologically, various techniques of cellular and molecular biology, as well as proteomic tools combined with bioinformatics, were utilized. Overall, the thesis provides interesting insights into inflammatory and antiviral innate immune responses, and has characterized host-virus interactions during influenza A virus-infection in human primary macrophages.

Artificial database for character recognition research

This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

A heuristic clustering algorithm using union of overlapping pattern-cells

Relative geometric arrangements of the sample points, with reference to the structure of the imbedding space, produce clusters. Hence, if each sample point is imagined to acquire a volume of a small M-cube (called pattern-cell), depending on the ranges of its (M) features and number (N) of samples; then overlapping pattern-cells would indicate naturally closer sample-points. A chain or blob of such overlapping cells would mean a cluster and separate clusters would not share a common pattern-cell between them. The conditions and an analytic method to find such an overlap are developed. A simple, intuitive, nonparametric clustering procedure, based on such overlapping pattern-cells is presented. It may be classified as an agglomerative, hierarchical, linkage-type clustering procedure. The algorithm is fast, requires low storage and can identify irregular clusters. Two extensions of the algorithm, to separate overlapping clusters and to estimate the nature of pattern distributions in the sample space, are also indicated.

A knowledge-based approach to pattern generation

Pattern Cognition is looked at from the functional view point. The need for knowledge in synthesizing such patterns is explained and various aspects of knowledge-based pattern generation are highlighted. This approach to the generation of patterns is detailed with a concrete example.

Character recognition — A review

The machine replication of human reading has been the subject of intensive research for more than three decades. A large number of research papers and reports have already been published on this topic. Many commercial establishments have manufactured recognizers of varying capabilities. Handheld, desk-top, medium-size and large systems costing as high as half a million dollars are available, and are in use for various applications. However, the ultimate goal of developing a reading machine having the same reading capabilities of humans still remains unachieved. So, there still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it. This review is organized into six major sections covering a general overview (an introduction), applications of character recognition techniques, methodologies in character recognition, research work in character recognition, some practical OCRs and the conclusions.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.