Adaptive Satellite Images Segmentation by Level Set Multiregion Competition

In this paper, we present an adaptive variational segmentation algorithm of spectral-texture regions in satellite images using level set. Satellite images contain both textured and non-textured regions, so for each region cues of spectral and texture are integrated according to their discrimination power. Motivated by Fisher-Rao's linear discriminant analysis, two region's weights are defined to code respectively the relevance of spectral and texture cues. Therefore, regions with or without texture are processed in the same framework. The obtained segmentation criterion is minimized via curves evolution within an explicit correspondence between the interiors of evolving curves and regions in segmentation. Thus, an unambiguous segmentation to a given arbitrary number of regions is obtained by the multiregion competition algorithm. Experimental results on both natural and satellite images are shown

(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

ANALYZING PULMONARY ABNORMALITY WITH SUPERPIXEL BASED GRAPH NEURAL NETWORKS IN CHEST X-RAY

In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. Convolutional Neural Network (CNN) has achieved state-of-the-art performance in areas such as computer vision, particularly for grid-like data such as images. However, they require a huge dataset to achieve top level of performance and challenge arises when learning from the inherent irregular/unordered nature of physiological data. In this thesis, the research primarily focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs (RAGs), and each superpixel serves as a dedicated graph node. For graph classification, provided that the different classes are distinct enough GNN often classify graphs using just the graph structures. This study delves into the inquiry of whether the incorporation of node features, such as coordinate points and pixel intensity, along with structured data representing graph can enhance the learning process. By integration of residual and concatenation structures, this methodology adeptly captures essential features and relationships among superpixels, thereby contributing to advancements in tuberculosis identification. We achieved the best performance: accuracy of 0.80 and AUC of 0.79, through the union of state-of-the-art neural network architectures and innovative graph-based representations. This work introduces a new perspective to medical image analysis

Microstructure Analysis and Surface Planarization of Excimer-laser Annealed Si Thin Films

The excimer-laser annealed (ELA) polycrystalline silicon (p-Si or polysilicon) thin film, which influences more than 100-billion-dollar display market, is the backplane material of the modern advanced LCD and OLED products. The microstructure (i.e. ELA microstructure) and surface morphology of an ELA p-Si thin film are the two main factors determining the material properties, and they significantly affect the performance of the subsequently fabricated thin film transistors (TFTs). The microstructure is the result of a rather complex crystallization process during the ELA which is characterized as far-from-equilibrium, multiple-pulse-per-area and processing-parameter dependent. Studies of the ELA microstructure and the surface morphology closely related to the device performance as well as the microstructure evolution during the ELA process are long-termly demanded by both the scientific research and the industrial applications, but unfortunately have not been thoroughly performed in the past. The main device-performance-related characteristics of the ELA microstructure are generally considered to be the grain size and the presence of the dense grain boundaries. In the work of this thesis, an image-processing-based program (referred to as the GB extraction program) is developed to extract the grain boundary map (GB map) out of the transmission electron microscope (TEM) images of the ELA microstructure. The grain sizes are straightforwardly calculated from the GB map and statistically analyzed. More importantly, based on the GB maps, we propose and perform a rigorous scheme that we call the local-microstructure analysis (LMA) to quantitatively and systematically analyze the spatial distribution of the grain boundaries. The “local area” is mainly defined by the geometry and the location of a TFT. The successful extraction of the GB map and the subsequent LMA are permitted by our unique TEM skills to produce high-resolution TEM micrographs containing statistically significant number of grains for sensible quantitative analysis. The LMA unprecedentedly enables quantitative and rigorous analysis of spatial characteristics of the microstructure, especially the device geometry- and location-related characteristics. Additionally, we present and highlight the benefits of the LMA approach over the traditional statistical grain-size analysis of the ELA microstructure. From the grain-size analysis, we find that grain size across a statistically significant number of grains generally follows the same distribution as in the stochastic grain growth scenario at the beginning of the ELA process when the laser pulse (i.e. shot) number is small. As the shot number increases, the overall grain size monotonically increases while the distribution profile becomes broader. When the scan number reaches the ELA threshold (several tens of laser shots), the distribution profile substantially deviates from the stochastic profile and shows two sharp peaks in grain size around 300nm and 450nm, which is consistent with the previously proposed theory of energy coupling and nonuniform energy deposition during ELA. From the LMA, local nonuniformity of grain boundary density (GB density) at the device length scales and regions of high grain boundary periodicity are identified. More importantly, we find that the local nonuniformity is much more pronounced when p-Si film exhibits some level of spatial ordering, but less pronounced for a random grain arrangement. It is worth noting that the devices of different sizes and orientation have different sensitivity to the local nonuniformity of the ELA-generated p-Si thin film. In addition, based on the analysis results, the connection between the microstructure evolution and the partial melting and resolidification process of the Si film is discussed. Aside from the microstructure, the surface morphology of the ELA films, featuring pronounced surface protrusions, is characterized via an atomic force microscope (AFM). Attempts to planarize those surface protrusions detrimental to the subsequent device performance are conducted. In the attempts, the as-is (oxide-capped) ELA films and the BHF-treated ELA films are subjected to single shots of excimer irradiation. When the results are compared, an anisotropic melting phenomenon of the p-Si grains is identified, which appears to be strongly affected by the presence of the surface oxide capping layer. Conceptual models are developed and numerical simulations are employed to explain the observation of the anisotropic melting phenomenon and the effect of the surface oxide layer. Eventually, 41.8% reduction of root mean square (RMS) surface roughness is achieved for BHF-treated ELA films. The results gained in the systematic analysis of the ELA microstructure and the attempt of surface planarization further our understanding about (1) the device performance-related material microstructure of the ELA p-Si thin films, (2) the microstructure evolution occurring during multiple shots of the ELA process, and (3) the fundamental phase transformations in the far-from-equilibrium melt-mediated excimer-laser annealing processing of p-Si thin films. Such understanding could help engineers when designing the microelectronic devices and the ELA manufacturing process, as well as provide scientific researchers with insights on the melting and solidification of general polycrystalline materials, thus profoundly contributing to both the related scientific society and the technological community. The GB extraction program and the LMA scheme developed and demonstrated in the thesis, as another contribution to the related research filed, could also be generalized to the microstructural study of other polycrystalline materials where grain geometry and arrangement are of concern