@mastersthesis {32764, title = {A Critical Examination of Two Specific Approaches Used to Characterize Medical Images: i) Projection-based Descriptors for Image Retrieval and ii) Estimating Fractal Dimensions of Discrete Sets}, year = {2022}, type = {PhD thesis}, abstract = {In this thesis we provide a critical examination of two methods which are used to characterize medical images. Accordingly, this thesis is split into two main parts. First, we take a look at the problem of designing efficient, compact image descriptors for content-based image retrieval of digital histopathology slides. Our approach here is twofold, in that we propose a frequency-based approach to encoding projection gradients and we study the effect of separating histology slides into two colour components based on a typical staining protocol. Our frequency-based approach is shown to be more effective in terms of search performance and efficiency than the standard MinMax method of binary encoding often employed in the literature.\ Furthermore, we find that by separating histopathology images into their stain components, we see a significant improvement in search accuracy over the use of greyscale images, and comparable, and often superior performance to the use of three channel RGB colour images as inputs. The results in this part of the thesis not only stand on their own as a solution for image search, they can also be applied to improve the efficiency and performance of future research in this field. In the second part of this thesis, we consider the use of fractal dimensions as a method to characterize vascular networks, and other branching structures such as streams, and trees. We discuss the self-similarity (or lack thereof) of branching structures, and provide a clear argument against the use of the typical methods, such as the box-counting and sandbox methods, to estimate fractal dimensions from finite images of branching networks. Additionally, local slopes are used as a tool to illustrate the issues with these approaches when they are applied to branching structures, such as computer-generated fractal trees and retinal vascular networks. Some alternative approaches are suggested which could be used for the characterization of complex branching structures, including vascular networks.}, url = {https://uwspace.uwaterloo.ca/handle/10012/18689}, author = {Alison Cheeseman} } @article {32765, title = {Estimating the Fractal Dimensions of Vascular Networks and Other Branching Structures: Some Words of Caution}, journal = {Mathematics}, volume = {10}, year = {2022}, abstract = {Branching patterns are ubiquitous in nature; consequently, over the years many researchers have tried to characterize the complexity of their structures. Due to their hierarchical nature and resemblance to fractal trees, they are often thought to have fractal properties; however, their non-homogeneity (i.e., lack of strict self-similarity) is often ignored. In this paper we review and examine the use of the box-counting and sandbox methods to estimate the fractal dimensions of branching structures. We highlight the fact that these methods rely on an assumption of self-similarity that is not present in branching structures due to their non-homogeneous nature. Looking at the local slopes of the log{\textendash}log plots used by these methods reveals the problems caused by the non-homogeneity. Finally, we examine the role of the canopies (endpoints or limit points) of branching structures in the estimation of their fractal dimensions.}, author = {Alison K. Cheeseman and Edward R. Vrscay} } @proceedings {29419, title = {Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance}, journal = {International Conference on Image Analysis and Recognition (ICIAR 2020)}, year = {2020}, pages = {262-273}, abstract = {Due to recent advances in technology, digitized histopathology images are now widely available for both clinical and research purposes. Accordingly, research into computerized image analysis algorithms for digital histopathology images has been progressing rapidly. In this work, we focus on image retrieval for digital histopathology images. Image retrieval algorithms can be used to find similar images and can assist pathologists in making quick and accurate diagnoses. Histopathology images are typically stained with dyes to highlight features of the tissue, and as such, an image analysis algorithm for histopathology should be able to process colour images and determine relevant information from the stain colours present. In this study, we are interested in the effect that stain separation into their individual stain components has on image search performance. To this end, we implement a basic k-nearest neighbours (kNN) search algorithm on histopathology images from two publicly available data sets (IDC and BreakHis) which are: a) converted to greyscale, b) digitally stain-separated and c) the original RGB colour images. The results of this study show that using H\&E separated images yields search accuracies within one or two percent of those obtained with original RGB images, and that superior performance is observed using the H\&E images in most scenarios we tested.}, url = {https://link.springer.com/chapter/10.1007/978-3-030-50516-5_23}, author = {Alison K. Cheeseman and Edward R. Vrscay and H.R. Tizhoosh} } @conference {22115, title = {A Compact Representation of Histopathology Images using Digital Stain Separation \& Frequency-Based Encoded Local Projections}, booktitle = {International Conference on Image Analysis and Recognition (ICIAR 2019)}, year = {2019}, pages = {147-158}, publisher = {Springer, Cham}, organization = {Springer, Cham}, abstract = {In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.}, url = {https://link.springer.com/chapter/10.1007/978-3-030-27272-2_13}, author = {Alison K. Cheeseman and Edward R. Vrscay and Hamid Tizhoosh} } @article {17780, title = {Design of multiple near-orthogonal spectrally-compliant waveforms via alternating successive convex approximations and projections}, journal = {IET Radar, Sonar \& Navigation}, volume = {13}, year = {2019}, pages = {781-788}, abstract = {The authors consider the design of multiple near-orthogonal transmit waveforms for a high-frequency surface wave radar (HFSWR) system operating in a congested spectral environment where the radar must adhere to strict regulations on the interference it can cause to on-going communication links. The HFSWR application necessitates multiple waveforms to increase the overall unambiguous radar range. Ideally, the waveforms would be constant amplitude, have low autocorrelation sidelobes, and low pulse-to-pulse cross-correlations, all while meeting the imposed spectral constraints; however, it is impossible to know a priori that such waveforms exist. They propose an algorithm based on alternating successive convex approximations and projections to design waveforms with low pulse-to-pulse cross-correlation which meet strict spectral and autocorrelation sidelobe constraints while minimising the amplitude modulation. In the simulation, the proposed algorithm is found to converge rapidly and when compared to similar methods from the recent literature, the proposed algorithm is found to generate waveforms with significantly lower peak-to-average power ratios and better pulse compression properties.}, author = {Alison Cheeseman and Raviraj S. Adve} } @mastersthesis {17779, title = {Adaptive Waveform Design and CFAR Processing for High Frequency Surface Wave Radar}, year = {2017}, type = {MASc Thesis}, abstract = {High frequency surface wave radar (HFSWR), used for coastal surveillance, operates in a challenging environment as the clutter signals returned from the ocean surface can be several orders of magnitude larger than returns from targets. Reliably detecting small boats in severe sea states, is therefore quite difficult. In this thesis we take a two-fold approach to improving the detection performance of Raytheon Canada{\textquoteright}s third generation HFSWR system. First, we consider the design of transmit waveforms with improved range resolution, thus reducing the area of the clutter cell. We develop an algorithm to design practical spectrally-compliant waveforms which achieve high bandwidths while simultaneously avoiding interference with concurrent communications users. We then propose a new detection algorithm based on the best known statistical model for sea clutter, the K-distribution. We show that both the proposed transmit waveforms and detection algorithm can lead to improved detection performance in a sea clutter environment.}, url = {https://tspace.library.utoronto.ca/handle/1807/79106}, author = {Alison Cheeseman} } @conference {17778, title = {Designing practical spectrally constrained waveforms for HFSWR by successive convex approximations and projections}, booktitle = {IET International Radar Conference}, year = {2017}, publisher = {IET Digital Library}, organization = {IET Digital Library}, address = {IET International Radar Conference, Belfast, UK}, abstract = {In this paper, we consider the design of practical transmit waveforms for a high frequency surface wave radar system operating in a congested spectral environment where the radar must adhere to strict regulations on the interference it can cause to on-going communications. The ideal waveform would be constant amplitude, and have low autocorrelation sidelobes, all while meeting the imposed spectral constraints; however, it is impossible to know a priori that such a waveform exists. We propose an algorithm based on alternating successive convex approximations and projections to design waveforms which meet strict spectral constraints and autocorrelation sidelobe constraints while minimizing amplitude modulation. In simulation, the proposed algorithm is found to converge rapidly and the resulting waveforms have better pulse compression properties than other similar methods from the relevant literature.}, author = {Alison Cheeseman and Raviraj S. Adve} } @conference {17777, title = {Objective Image Quality Measures of Degradation in Compressed Natural Images and their Comparison with Subjective Assessments}, booktitle = {International Conference on Image Analysis and Recognition (ICIAR)}, year = {2016}, pages = {163-172}, abstract = {

This paper is concerned with the degradation produced in natural images by JPEG compression. Our study has been basically twofold: (i) To find relationships between the amount of compression-induced degradation in an image and its various statistical properties. The goal is to identify blocks that will exhibit lower/higher rates of degradation as the degree of compression increases. (ii) To compare the above\ objective\ characterizations with\ subjective\ assessments of observers.

The conclusions of our study are rather significant in several aspects. First of all, {\textquotedblleft}bad{\textquotedblright} blocks, i.e., blocks exhibiting greater degrees of degradation visually, have among the lowest RMSEs of all blocks and among the medium-to-highest structural similarity (SSIM)-based errors. Secondly, the standard deviations of {\textquotedblleft}bad{\textquotedblright} blocks are among the lowest of all blocks, suggesting a kind of {\textquotedblleft}Weber law for compression,{\textquotedblright} a consequence of contrast masking. Thirdly, {\textquotedblleft}bad{\textquotedblright} blocks have medium-to-high high-frequency (HF) fractions as opposed to HF content.

}, author = {Alison K. Cheeseman and Ilona A. Kowalik-Urbaniak and Edward R. Vrscay} }