Ryan Lagerstrom

ICE: a statistical approach to identifying endmembers in hyperspectral images

Several of the more important endmember-finding algorithms for hyperspectral data are discussed and some of their shortcomings highlighted. A new algorithm - iterated constrained endmembers (ICE) - which attempts to address these shortcomings is introduced. An example of its use is given. There is also a discussion of the advantages and disadvantages of normalizing spectra before the application of ICE or other endmember-finding algorithms.

Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision

Manual neuron tracing is a very labor‐intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA‐Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA‐Vision with those obtained using an established semi‐automated tracing solution (NeuronJ). The effect of the Sez‐6 deletion was characterized in detail. Sez‐6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA‐Vision delivered considerable speed benefits and reliable traces.

HCA-Vision Automated Neurite Outgrowth Analysis

Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at http://www.hca-vision.com.

Boundary extraction of linear features using dual paths through gradient profiles

An algorithm for automated extraction of linear feature boundaries in 2D images is presented. From a marker set approximating the medial axis, we generate 1D gradient profiles orthogonal to linear features. The algorithm uses dual shortest paths through an image generated from gradient profiles to extract boundaries. The algorithm offers an alternative to a watershed type approach and performs well on images with noise and areas of low contrast. We present the results of our algorithm on microscopy images of neurite outgrowth and other images containing linear features.

Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey

We describe an investigation into how Massey Universitys Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National Universitys pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynders native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Automated Opal Grading by Imaging and Statistical Learning

Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals-the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment workflow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.

Image Classification to Support Emergency Situation Awareness

Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of emergency situation awareness. We discuss image classification based on low level features as well as methods built on top of pre-trained classifiers. The performance of the classifiers are assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire related classes with an accuracy of 86%.

A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2890 grains, 15...

A Comparison Between Three Sparse Unmixing Algorithms Using a Large Library of Shortwave Infrared Mineral Spectra

The comparison described in this paper has been motivated by two things: 1) a “spectral library” of shortwave infrared reflectance spectra that we have built, consisting of the spectra of 60 nominally pure materials (mostly minerals, but also water, dry vegetation, and several man-made materials) and 2) the needs of users in the mining industry for the use of fast and accurate unmixing software to analyze tens to hundreds of thousands of spectra measured from drill core or chips using HyLogging instruments, and other commercial reflectance spectrometers. Individual samples are typically a mixture of only one, two, three, or occasionally four minerals. Therefore, in order to avoid overfitting, a sparse unmixing algorithm is required. We compare three such algorithms using some real world test data sets: full subset selection (FSS), sparse demixing (SD), and L1 regularization. To aid the comparison, we introduce two novel aspects: 1) the simultaneous fitting of the low frequency background with mineral identification (which provides greater model flexibility) and 2) the combined fitting being carried out using a suitably defined Mahalanobis distance; this has certain optimality properties under an idealized model. Together, these two innovations significantly improve the accuracy of the results. FSS and L1 regularization (suitably optimized) produce similar levels of accuracy, and are superior to SD. Discussion includes possible improvements to the algorithms, and their possible use in other domains.

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography.

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.