Spatially varying threshold models for the automated segmentation of radiodense tissue in digitized mammograms

The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women\u27s Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique

Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing

Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e.g., Compressively Sensed Measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the Convolutional Support Estimator Networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: (i) Real-time and low-cost support estimation can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, etc. (ii) CSEN's output can directly be used as "prior information" which improves the performance of sparse signal recovery algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.Comment: 12 page

On the monotone and primal-dual active set schemes for ℓp\ell^p-type problems, p∈(0,1]p \in (0,1]

Nonsmooth nonconvex optimization problems involving the $\ell^p$ quasi-norm, $p \in (0, 1]$, of a linear map are considered. A monotonically convergent scheme for a regularized version of the original problem is developed and necessary optimality conditions for the original problem in the form of a complementary system amenable for computation are given. Then an algorithm for solving the above mentioned necessary optimality conditions is proposed. It is based on a combination of the monotone scheme and a primal-dual active set strategy. The performance of the two algorithms is studied by means of a series of numerical tests in different cases, including optimal control problems, fracture mechanics and microscopy image reconstruction

Mathematical Methods for the Quantification of Actin-Filaments in Microscopic Images

In cell biology confocal laser scanning microscopic images of the actin filament of human osteoblasts are produced to assess the cell development. This thesis aims at an advanced approach for accurate quantitative measurements about the morphology of the bright-ridge set of these microscopic images and thus about the actin filament. Therefore automatic preprocessing, tagging and quantification interplay to approximate the capabilities of the human observer to intuitively recognize the filaments correctly. Numerical experiments with random models confirm the accuracy of this approach

Sensitivity Methods Applied to Orbital Pursuit-Evasion

In this work, sensitivity methods are examined as a means to solve and analyze the problem of orbital pursuit-evasion (PE). Orbital PE is a two-sided spacecraft trajectory optimization problem characterized by high dimensionality and nonlinearity. Modern methods for solving problems of this sort employ generic, computationally intensive techniques, including random search methods such as the genetic algorithm; collocation methods based on discrete approximation; or combinations of these methods. The advantages of these methods are relatively high degrees of robustness, straightforward implementation, and ease of handling state and control constraints. Yet we note the disadvantages: chiefly high computation load, as well as absence of insight into the problem, and accuracy of the result. Sensitivity methods provide corresponding strengths in each of these areas. We present novel sensitivity analysis techniques that may be useful in other optimization problems featuring high dimensionality, nonlinearity, and/or state and control constraints. The techniques shown include a novel solution method; a computationally efficient feedback control technique; a means of sketching barrier surfaces; and the use of hybrid one-sided/two-sided controllers for sophisticated emergent behavior. We also introduce a new formulation of the problem incorporating a minimum-altitude constraint, and we make an initial investigation of a sensitivity-based method of handling state constraints. Overall, our results suggest that sensitivity methods can provide useful augmentation to techniques that rely more heavily upon computational power, and may be particularly valuable for implementation in an onboard control algorithm