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

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Scene segmentation using similarity, motion and depth based cues

Segmentation of complex scenes to aid surveillance is still considered an open research problem. In this thesis a computational model (CM) has been developed to classify a scene into foreground, moving-shadow and background regions. It has been demonstrated how the CM, with the optional use of a channel ratio test, can be applied to demarcate foreground shadow regions in indoor scenes illuminated by a fixed incandescent source of light. A combined approach, involving the CM working in tandem with a traditional motion cue based segmentation method, has also been constructed. In the combined approach, the CM is applied to segregate the foreground shaded regions in a current frame based on a binary mask generated using a standard background subtraction process (BSP). Various popular outlier detection strategies have been investigated to assess their suitabilities in generating a threshold automatically, required to develop a binary mask from a difference frame, the outcome of the BSP. To evaluate the full scope of the pixel labeling capabilities of the CM and to estimate the associated time constraints, the model is deployed for foreground scene segmentation in recorded real-life video streams. The observations made validate the satisfactory performance of the model in most cases. In the second part of the thesis depth based cues have been exploited to perform the task of foreground scene segmentation. An active structured light based depthestimating arrangement has been modeled in the thesis; the choice of modeling an active system over a passive stereovision one has been made to alleviate some of the difficulties associated with the classical correspondence problem. The model developed not only facilitates use of the set-up but also makes possible a method to increase the working volume of the system without explicitly encoding the projected structured pattern. Finally, it is explained how scene segmentation can be accomplished based solely on the structured pattern disparity information, without generating explicit depthmaps. To de-noise the difference frames, generated using the developed method, two median filtering schemes have been implemented. The working of one of the schemes is advocated for practical use and is described in terms of discrete morphological operators, thus facilitating hardware realisation of the method to speed-up the de-noising process

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed