Towards Fast and High-quality Biomedical Image Reconstruction

Department of Computer Science and EngineeringReconstruction is an important module in the image analysis pipeline with purposes of isolating the majority of meaningful information that hidden inside the acquired data. The term ???reconstruction??? can be understood and subdivided in several specific tasks in different modalities. For example, in biomedical imaging, such as Computed Tomography (CT), Magnetic Resonance Image (MRI), that term stands for the transformation from the, possibly fully or under-sampled, spectral domains (sinogram for CT and k-space for MRI) to the visible image domains. Or, in connectomics, people usually refer it to segmentation (reconstructing the semantic contact between neuronal connections) or denoising (reconstructing the clean image). In this dissertation research, I will describe a set of my contributed algorithms from conventional to state-of-the-art deep learning methods, with a transition at the data-driven dictionary learning approaches that tackle the reconstruction problems in various image analysis tasks.clos

CUDA based Level Set Method for 3D Reconstruction of Fishes from Large Acoustic Data

Acoustic images present views of underwater dynamics, even in high depths. With multi-beam echo sounders (SONARs), it is possible to capture series of 2D high resolution acoustic images. 3D reconstruction of the water column and subsequent estimation of fish abundance and fish species identification is highly desirable for planning sustainable fisheries. Main hurdles in analysing acoustic images are the presence of speckle noise and the vast amount of acoustic data. This paper presents a level set formulation for simultaneous fish reconstruction and noise suppression from raw acoustic images. Despite the presence of speckle noise blobs, actual fish intensity values can be distinguished by extremely high values, varying exponentially from the background. Edge detection generally gives excessive false edges that are not reliable. Our approach to reconstruction is based on level set evolution using Mumford-Shah segmentation functional that does not depend on edges in an image. We use the implicit function in conjunction with the image to robustly estimate a threshold for suppressing noise in the image by solving a second differential equation. We provide details of our estimation of suppressing threshold and show its convergence as the evolution proceeds. We also present a GPU based streaming computation of the method using NVIDIA’s CUDA framework to handle large volume data-sets. Our implementation is optimised for memory usage to handle large volumes

Uses of Complex Wavelets in Deep Convolutional Neural Networks

Image understanding has long been a goal for computer vision. It has proved to be an exceptionally difficult task due to the large amounts of variability that are inherent to objects in a scene. Recent advances in supervised learning methods, particularly convolutional neural networks (CNNs), have pushed forth the frontier of what we have been able to train computers to do. Despite their successes, the mechanics of how these networks are able to recognize objects are little understood, and the networks themselves are often very difficult and time-consuming to train. It is very important that we improve our current approaches in every way possible. A CNN is built from connecting many learned convolutional layers in series. These convolutional layers are fairly crude in terms of signal processing - they are arbitrary taps of a finite impulse response filter, learned through stochastic gradient descent from random initial conditions. We believe that if we reformulate the problem, we may achieve many insights and benefits in training CNNs. Noting that modern CNNs are mostly viewed from and analyzed in the spatial domain, this thesis aims to view the convolutional layers in the frequency domain (viewing things in the frequency domain has proved useful in the past for denoising, filter design, compression and many other tasks). In particular, we use complex wavelets (rather than the Fourier transform or the discrete wavelet transform) as basis functions to reformulate image understanding with deep networks. In this thesis, we explore the most popular and well-developed form of using complex wavelets in deep learning, the ScatterNet from Stephane Mallat. We explore its current limitations by building a DeScatterNet and found that while it has many nice properties, it may not be sensitive to the most appropriate shapes for understanding natural images. We then develop a locally invariant convolutional layer, a combination of a complex wavelet transform, a modulus operation, and a learned mixing. To do this, we derive backpropagation equations and allow gradients to flow back through the (previously fixed) ScatterNet front end. Connecting several such locally invariant layers allows us to build learnable ScatterNet, a more flexible and general form of the ScatterNet (while still maintaining its desired properties). We show that the learnable ScatterNet can provide significant improvements over the regular ScatterNet when being used as a front end for a learning system. Additionally, we show that the locally invariant convolutional layer can directly replace convolutional layers in a deep CNN (and not just at the front-end). The locally invariant convolutional layers naturally downsample the input (because of the complex modulus) while increasing the channel dimension (because of the multiple wavelet orientations used). This is an operation that often happens in a CNN by a combination of a pooling and convolutional layer. It was at these locations in a CNN where the learnable ScatterNet performed best, implying it may be useful as learnable pooling layer. Finally, we develop a system to learn complex weights that act directly on the wavelet coefficients of signals, in place of a convolutional layer. We call this layer the wavelet gain layer and show it can be used alongside convolutional layers. The network designer may then choose to learn in the pixel or wavelet domains. This layer shows a lot of promise and affords more control over what regions of the frequency space we want our layer to learn from. Our experiments show that it can improve on learning in the pixel domain for early layers of a CNN

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin