Systematic Characterization Framework of Row-Column Ultrasound Imaging Systems

3-D ultrasound imaging offers unique opportunities in the field of non-destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges which cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependant point spread function of the imaging system. There is no characterization model that describes these intrinsic challenges. In this research, we will propose a characterization framework for ultrasound imaging systems that are based on the row column method. The proposed framework will include a joint statistical image formation and noise modeling and characterization as well as a characterization of the system's beam profile using a spatially-variant point spread function. Our proposed framework has many potential applications including building a more adequate image reconstruction model, providing a better metric for comparison of different row column systems, allowing for a better optimization of a row column system's performance, and giving us a better understanding of images acquired from row column systems

Compensated Row-Column Ultrasound Imaging System

Ultrasound imaging is a valuable tool in many applications ranging from material science to medical imaging. While 2-D ultrasound imaging is more commonly used, 3-D ultrasound imaging offers unique opportunities that can only be found with the help of the extra dimension. Acquiring a 3-D ultrasound image can be done in two main ways: mechanically moving a transducer over a region of interest and using a fixed 2-D transducer. Mechanical motion introduces unwanted artifacts and increases image acquisition time, so a fixed 2-D is usually preferred. However, a fully addressed 2-D array will require a significant amount of connections and data to handle. This motivated the exploration of different simplification schemes to make 2-D arrays for 3-D ultrasound imaging feasible. A method that received a lot of attention for making real-time volumetric ultrasound imaging possible is the row-column method. The row-column method simplifies the fully addressed 2-D array by utilizing a set of 1-D arrays arranged in rows and another set in columns, one set will be responsible for transmit beamforming, while the other for receive beamforming. Using this setup, only $N+N$ connections are needed instead of $N\times N$. This simplification comes at the cost of image quality. Recent advances in row-column ultrasound imaging systems were largely focused on transducer design. However, these imaging systems face a few intrinsic challenges which cannot be addressed through transducer design alone: the issues of sparsity, speckle noise inherent to ultrasound, the spatially varying point spread function, and the ghosting artifacts inherent to the row-column method must all be taken into account. As such, strategies for tackling these intrinsic challenges in row-column imaging would be highly desired to improve imaging quality. In this thesis, we propose a novel compensated row-column ultrasound imaging system where the intrinsic characteristics of the transducer and other aspects of the physical row-column imaging apparatus are leveraged to computationally produce high quality ultrasound imagery. More specifically, the proposed system incorporates a novel conditional random field-driven computational image reconstruction component consisting of two phases: i) characterization and ii) compensation. In the characterization phase, a joint statistical image formation and noise model is introduced for characterizing the intrinsic properties of the physical row-column ultrasound imaging system. In the compensation phase, the developed joint image formation and noise model is incorporated alongside a conditional random field model within an energy minimization framework to reconstruct the compensated row-column ultrasound imagery. To explore the efficacy of the proposed concept, we introduced three different realizations of the proposed compensated row-column ultrasound imaging system. First, we introduce a compensated row-column imaging system based on a novel multilayered conditional random field driven framework to better account for local spatial relationships in the captured data. Second, we incorporated more global relationships by introducing a compensated row-column imaging system based around a novel edge-guided stochastically fully connected random field framework. Third, accounting for the case where the analytical image formation model may not optimally reflect the real-world physical system, we introduce a compensated row-column imaging system based around a data-driven spatially varying point-spread-function learning framework to better characterize the true physical image formation characteristics. While these different realizations of the compensated row-column system have their advantages and disadvantages, which will be discussed throughout this thesis, they all manage to boost the performance of the row-column method to comparable and often higher levels than the fully addressed 2-D array

Automated Histological Analysis System for Quantifying Microstructural Damage Accumulation to the Annulus Fibrosus

Ben Daya, I., Noguchi, M., Callaghan, J. P., & Wong, A. (2016). Automated Histological Analysis System for Quantifying Microstructural Damage Accumulation to the Annulus Fibrosus. Journal of Computational Vision and Imaging Systems, 2(1). Retrieved from http://openjournals.uwaterloo.ca/index.php/vsl/article/view/100In this paper, we proposed an automated histological analysis system for quantifying microstructural damage accumulation to the annulus fibrosus. This system takes in a digital histology image and uses Gaussian mixture model based segmentation, followed by connected components analysis to extract and label possible clefts. The image is then refined through spatial and size constraints. Finally, the required statistics for quantifying microstructural damage are calculated.This research was funded by the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and the Ontario Ministry of Research and Innovation

On Robustness of Deep Neural Networks: A Comprehensive Study on the Effect of Architecture and Weight Initialization to Susceptibility and Transferability of Adversarial Attacks

Neural network models have shown state of the art performance inseveral applications. However it has been observed that they aresusceptible to adversarial attacks: small perturbations to the inputthat fool a network model into mislabelling the input data. Theseattacks can also transfer from one network model to another, whichraises concerns over their applicability, particularly when there areprivacy and security risks involved. In this work, we conduct a studyto analyze the effect of network architectures and weight initial-ization on the robustness of individual network models as well astransferability of adversarial attacks. Experimental results demon-strate that while weight initialization has no affect on the robustnessof a network model, it does have an affect on attack transferabilityto a network model. Results also show that the complexity of anetwork model as indicated by the total number of parameters andMAC number is not indicative of a network’s robustness to attackor transferability, but accuracy can be; within the same architec-ture, higher accuracy usually indicates a more robust network, butacross architectures there is no strong link between accuracy androbustness

Compensated Row-Column Ultrasound Imaging System Using Fisher Tippett Multilayered Conditional Random Field Model

Ben Daya I, Chen AIH, Shafiee MJ, Wong A, Yeow JTW (2015) Compensated Row-Column Ultrasound Imaging System Using Fisher Tippett Multilayered Conditional Random Field Model. PLoS ONE 10(12): e0142817. doi:10.1371/journal.pone.01428173-D ultrasound imaging offers unique opportunities in the field of non destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges that cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependent point spread function of the imaging system. In this paper, we propose a compensated row-column ultrasound image reconstruction system using Fisher-Tippett multilayered conditional random field model. Tests carried out on both simulated and real row-column ultrasound images show the effectiveness of our proposed system as opposed to other published systems. Visual assessment of the results show our proposed system’s potential at preserving detail and reducing speckle. Quantitative analysis shows that our proposed system outperforms previously published systems when evaluated with metrics such as Peak Signal to Noise Ratio, Coefficient of Correlation, and Effective Number of Looks. These results show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.This research was funded by the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and the Ontario Ministry of Research and Innovation

Compensated Row-Column Ultrasound Imaging System Using Edge-Guided Three Dimensional Random Fields

The row-column method is a simplification technique used to reducethe complexity of a fully addressed 2-D array. Although itgreatly reduces the number of physical connections required aswell as the amount of data to be handled, it still has limitations; itsimaging data output is sparse, it suffers from speckle noise, and itsspatially-dependant point spread function is riddled with edge artifacts.In this work, we propose a row-column ultrasound imagingsystem, termed CRUIS3D, that uses a 3-D edge-guided randomfield approach to compensate for the limitations of the row-columnmethod. Tests on CRUIS3D and previously published row-columnsystems show the effectiveness of our proposed system as a toolfor enhancing 3-D row-column ultrasound imaging

Text Enhancement in Projected Imagery

There is great interest in improving the visual quality of projectedimagery. In particular, for image enhancement, we would assertthat text and non-text regions should be enhanced differently inseeking to maximize perceived quality, since the spatial and statis-tical characteristics of text and non-text images are quite distinct.In this paper, we present a text enhancement scheme based on anovel local dynamic range statistical thresholding. Given an inputimage, text-like regions are obtained on the basis of computing thelocal statistics of regions having a high dynamic range, allowing apixel-wise classification into text-like or background classes. Theactual enhancement is obtained via class-dependent Wiener filter-ing, with text-like regions sharpened more than the background.Experimental results on four challenging images show that the pro- posed scheme offers a better visual quality than projection with- out enhancement as well as a recent state-of-the-art enhancementmethod