Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Unsupervised Generative Modeling Using Matrix Product States

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning analogous to the density matrix renormalization group method, which allows dynamically adjusting dimensions of the tensors and offers an efficient direct sampling approach for generative tasks. We apply our method to generative modeling of several standard datasets including the Bars and Stripes, random binary patterns and the MNIST handwritten digits to illustrate the abilities, features and drawbacks of our model over popular generative models such as Hopfield model, Boltzmann machines and generative adversarial networks. Our work sheds light on many interesting directions of future exploration on the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to be realized on quantum devices.Comment: 11 pages, 12 figures (not including the TNs) GitHub Page: https://congzlwag.github.io/UnsupGenModbyMPS

An MS Windows prototype for automatic general purpose image-based flaw detection

Flaw detection plays a crucial role in many industries to make sure that the products meet the specified quality requirements. When making for example a car it is important that all the parts satisfy certain quality standards to make sure the consumer buys a car that is safe to operate. A crack or another weakness in a crucial part can be catastrophic. To make sure their cars are as safe as possible, car manufacturers are conducting thorough testing of crucial parts. Similar tests are done in a wide variety of industries, and these quality controls are often referred to as flaw detection. Any cracks, voids, or other weaknesses that can cause danger are called flaws. Flaw detection is often done, or preferred done, in real time-- in an assembly line fashion. An important constraint, in addition to reliability, is therefore speed. The techniques used in these tests varies. Common techn~ques are ultrasonic waves (1-D or 2-D), eddy current imaging, x-ray imaging, thermal imaging, and fluorescent penetrent imaging. In this thesis I will discuss automatic general purpose image-based flaw detection. Automatic means that the flaw detection is performed without human supervision, and general purpose means that the inspection is not tailored to a specific task (i.e. one particular flaw in one particular type of object), but is ideally applicable to any detection problem

Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results

Development of Deep Learning Techniques for Image Retrieval

Images are used in many real-world applications, ranging from personal photo repositories to medical imaging systems. Image retrieval is a process in which the images in the database are first ranked in terms their similarities with respect to a query image, then a certain number of the images are retrieved from the ranked list that are most similar to the query image. The performance of an image retrieval algorithm is measured in terms of mean average precision. There are numerous applications of image retrieval. For example, face retrieval can help identify a person for security purposes, medical image retrieval can help doctors make more informed medical diagnoses, and commodity image retrieval can help customers find desired commodities. In recent years, image retrieval has gained more popularity in view of the emergence of large-capacity storage devices and the availability of low-cost image acquisition equipment. On the other hand, with the size and diversity of image databases continuously growing, the task of image retrieval has become increasingly more complex. Recent image retrieval techniques have focused on using deep learning techniques because of their exceptional feature extraction capability. However, deep image retrieval networks often employ very complex networks to achieve a desired performance, thus limiting their practicability in applications with limited storage and power capacity. The objective of this thesis is to design high-performance, low complexity deep networks for the task of image retrieval. This objective is achieved by developing three different low-complexity strategies for generating rich sets of discriminating features. Spatial information contained in images is crucial for providing detailed information about the positioning and interrelation of various elements within an image and thus, it plays an important role in distinguishing different images. As a result, designing a network to extract features that characterize this spatial information within an image is beneficial for the task of image retrieval. In the light of the importance of spatial information, in our first strategy, we develop two deep convolutional neural networks capable of extracting features with a focus on the spatial information. For the design of the first network, multi-scale dilated convolution operations are used to extract spatial information, whereas in the design of the second network, fusion of feature maps obtained from different hierarchical levels are employed to extract spatial information. Textural, structural, and edge information is very important for distinguishing images, and therefore, a network capable of extracting features characterizing this type of information about the images could be very useful for the task of image retrieval. Hence, in our second strategy, we develop a deep convolutional neural network that is guided to extract textural, structural, and edge information contained in an image. Since morphological operations process the texture and structure of the objects within an image based on their geometrical properties and edges are fundamental features of an image, we use morphological operations to guide the network in extracting textural and structural information, and a novel pooling operation for extracting the edge information in an image. Most of the researchers in the area of image retrieval have focused on developing algorithms aimed at yielding good retrieval performance at low computational complexity by outputting a list of certain number of images ranked in a decreasing order of similarity with respect to the query image. However, there are other researchers who have adopted a course of improving the results of an already existing image retrieval algorithm through a process of a re-ranking technique. A re-ranking scheme for image retrieval accesses the list of the images retrieved by an image retrieval algorithm and re-ranks them so that the re-ranked list at the output the scheme has a mean average precision value higher than that of the originally retrieved list. A re-ranking scheme is an overhead to the process of image retrieval, and therefore, its complexity should be as small as possible. Most of the re-ranking schemes in the literature aim to boost the retrieval performance at the expense of a very high computational complexity. Therefore, in our third strategy, we develop a computationally efficient re-ranking scheme for image retrieval, whose performance is superior to that of the existing re-ranking schemes. Since image hashing offers the dual benefits of computational efficiency and the ability to generate versatile image representation, we adopt it in the proposed re-ranking scheme. Extensive experiments are performed, in this thesis, using benchmark datasets, to demonstrate the effectiveness of the proposed new strategies in designing low-complexity deep networks for image retrieval

Modeling multiple object scenarios for feature recognition and classification using cellular neural networks

Cellular neural networks (CNNs) have been adopted in the spatio-temporal processing research field as a paradigm of complexity. This is due to the ease of designs for complex spatio-temporal tasks introduced by these networks. This has led to an increase in the adoption of CNNs for on-chip VLSI implementations. This dissertation proposes the use of a Cellular Neural Network to model, detect and classify objects appearing in multiple object scenes. The algorithm proposed is based on image scene enhancement through anisotropic diffusion; object detection and extraction through binary edge detection and boundary tracing; and object classification through genetically optimised associative networks and texture histograms. The first classification method is based on optimizing the space-invariant feedback template of the zero-input network through genetic operators, while the second method is based on computing diffusion filtered and modified histograms for object classes to generate decision boundaries that can be used to classify the objects. The primary goal is to design analogic algorithms that can be used to perform these tasks. While the use of genetically optimized associative networks for object learning yield an efficiency of over 95%, the use texture histograms has been found very accurate though there is a need to develop a better technique for histogram comparisons. The results found using these analogic algorithms affirm CNNs as well-suited for image processing tasks

Three-dimensional object recognition

In the development of an object pattern recognition system, feature construction is always the problem issue. Due to the large amount of information contained in three dimensional (3D) objects, features extracted to efficiently and sufficiently represent 3D objects are difficult to obtain. Thus, current commercially available object recognition systems mostly emphasize the classification of two dimensional objects or patterns. This work presents a paradigm to develop a complete 3D object recognition system that uses simple and efficient features, and supports the integration of CAD/CAM models;In this research, several proposed algorithm for extracting features representing 3D objects are constructed based on the properties of the Radon transform. Two of these algorithms have been successfully implemented for manufacturing applications. The implemented systems use the artificial neural network as the classifier to learn features and to identify 3D objects. A statistical model has also been established based on the output node values of a perceptron neural network to predict the future misclassifications of features which have not been learned by the neural network in the training stage