Leanne Bischof

Seeded region growing

We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >

The genomic and phenotypic diversity of Schizosaccharomyces pombe

Natural variation within species reveals aspects of genome evolution and function. The fission yeast Schizosaccharomyces pombe is an important model for eukaryotic biology, but researchers typically use one standard laboratory strain. To extend the usefulness of this model, we surveyed the genomic and phenotypic variation in 161 natural isolates. We sequenced the genomes of all strains, finding moderate genetic diversity (π = 3 × 10−3 substitutions/site) and weak global population structure. We estimate that dispersal of S. pombe began during human antiquity (∼340 BCE), and ancestors of these strains reached the Americas at ∼1623 CE. We quantified 74 traits, finding substantial heritable phenotypic diversity. We conducted 223 genome-wide association studies, with 89 traits showing at least one association. The most significant variant for each trait explained 22% of the phenotypic variance on average, with indels having larger effects than SNPs. This analysis represents a rich resource to examine genotype-phenotype relationships in a tractable model.

Detection of tubule boundaries based on circular shortest path and polar‐transformation of arbitrary shapes

In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton‐based polar‐transformation, boundary weighting of polar‐transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.

GrainScan: a low cost, fast method for grain size and colour measurements

BackgroundMeasuring grain characteristics is an integral component of cereal breeding and research into genetic control of seed development. Measures such as thousand grain weight are fast, but do not give an indication of variation within a sample. Other methods exist for detailed analysis of grain size, but are generally costly and very low throughput. Grain colour analysis is generally difficult to perform with accuracy, and existing methods are expensive and involved.ResultsWe have developed a software method to measure grain size and colour from images captured with consumer level flatbed scanners, in a robust, standardised way. The accuracy and precision of the method have been demonstrated through screening wheat and Brachypodium distachyon populations for variation in size and colour.ConclusionBy using GrainScan, cheap and fast measurement of grain colour and size will enable plant research programs to gain deeper understanding of material, where limited or no information is currently available.

Estimating band-to-band misregistrations in aliased imagery

Abstract We compare two classes of techniques, cross-covariance-based and Fourier-based, for estimating band-to-band misregistrations in multispectral imagery. We show that both methods often give biased estimates of the misregistrations, the former because of inadequate interpolation procedures and the latter because they do not account for the presence of aliasing. Such aliasing is often present, especially in remote sensing imagery. We describe a Fourier-based method that accounts for aliasing and that, for a variety of 512 × 512 image pairs, gives misregistration estimates with standard errors quite often less than 1/100th of a pixel in both horizontal and vertical directions. The theory is applied to one artificial and three real image pairs, thus demonstrating some of its practical consequences. There is also a brief discussion of the implications of the theory for image registration.

A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

Highlight Fluorescence imaging was built into a portable box called BlueBox, and roots in soil cores were directly and accurately quantified by automated image analysis, allowing root phenotyping in the field for pre-breeding.

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.

Validation of an Algorithm to Quantify Changes in Actin Cytoskeletal Organization

The actin cytoskeleton plays an important role in most, if not all, processes necessary for cell survival. Given the fundamental role that the actin cytoskeleton plays in the progression of cancer, it is an ideal target for chemotherapy. Although it is possible to image the actin cytoskeleton in a high-throughput manner, there is currently no validated method to quantify changes in the cytoskeleton in the same capacity, which makes research into its organization and the development of anticytoskeletal drugs difficult. We have validated the use of a linear feature detection algorithm, allowing us to measure changes in actin filament organization. Its ability to quantify changes associated with cytoskeletal disruption will make it a valuable tool in the development of compounds that target the cytoskeleton in cancer. Our results show that this algorithm can quantify cytoskeletal changes in a cell-based system after addition of both well-established and novel anticytoskeletal agents using either fluorescence microscopy or a high-content imaging approach. This novel method gives us the potential to screen compounds in a high-throughput manner for cancer and other diseases in which the cytoskeleton plays a key role.

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.

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...