Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Segmentation of the Thalamus in Multi-Spectral MR Images Using a Combination of Atlas-Based and Gradient Graph Cut Methods

International audienceTwo popular segmentation methods used today are atlas based and graph cut based segmentation techniques. The atlas based method deforms a manually segmented image onto a target image, resulting in an automatic segmentation. The graph cut segmentation method utilizes the graph cut paradigm by treating image segmentation as a max-flow problem. A specialized form of this algorithm was developed by Lecoeur et al [1], called the spectral graph cut algorithm. The goal of this paper is to combine both of these methods, creating a more stable atlas based segmentation algorithm that is less sensitive to the initial manual segmentation. The registration algorithm is used to automate and initialize the spectral graph cut algorithm as well as add needed spatial information, while the spectral graph cut algorithm is used to increase the robustness of the atlas method. To calculate the sensitivity of the algorithms, the initial manual segmentation of the atlas was both dilated and eroded 2 mm and the segmentation results were calculated. Results show that the atlas based segmentation segments the thalamus well with an average Dice Similarity Coefficient (DSC) of 0.87. The spectral graph cut method shows similar results with an average DSC measure of 0.88, with no statistical difference between the two methods. The atlas based method's DSC value, however, was reduced to 0.76 and 0.67 when dilated and eroded respectively, while the combined method retained a DSC value of 0.81 and 0.74, with a statistical difference found between the two methods

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Relevant data representation by a Kernel-based framework

Nowadays, the analysis of a large amount of data has emerged as an issue of great interest taking increasing place in the scientific community, especially in automation, signal processing, pattern recognition, and machine learning. In this sense, the identification, description, classification, visualization, and clustering of events or patterns are important problems for engineering developments and scientific issues, such as biology, medicine, economy, artificial vision, artificial intelligence, and industrial production. Nonetheless, it is difficult to interpret the available information due to its complexity and a large amount of obtained features. In addition, the analysis of the input data requires the development of methodologies that allow to reveal the relevant behaviors of the studied process, particularly, when such signals contain hidden structures varying over a given domain, e.g., space and/or time. When the analyzed signal contains such kind of properties, directly applying signal processing and machine learning procedures without considering a suitable model that deals with both the statistical distribution and the data structure, can lead in unstable performance results. Regarding this, kernel functions appear as an alternative approach to address the aforementioned issues by providing flexible mathematical tools that allow enhancing data representation for supporting signal processing and machine learning systems. Moreover, kernelbased methods are powerful tools for developing better-performing solutions by adapting the kernel to a given problem, instead of learning data relationships from explicit raw vector representations. However, building suitable kernels requires some user prior knowledge about input data, which is not available in most of the practical cases. Furthermore, using the definitions of traditional kernel methods directly, possess a challenging estimation problem that often leads to strong simplifications that restrict the kind of representation that we can use on the data. In this study, we propose a data representation framework based on kernel methods to learn automatically relevant sample relationships in learning systems. Namely, the proposed framework is divided into five kernel-based approaches, which aim to compute relevant data representations by adapting them according to both the imposed sample relationships constraints and the learning scenario (unsupervised or supervised task). First, we develop a kernel-based representation approach that allows revealing the main input sample relations by including relevant data structures using graph-based sparse constraints. Thus, salient data structures are highlighted aiming to favor further unsupervised clustering stages. This approach can be viewed as a graph pruning strategy within a spectral clustering framework which allows enhancing both the local and global data consistencies for a given input similarity matrix. Second, we introduce a kernel-based representation methodology that captures meaningful data relations in terms of their statistical distribution. Thus, an information theoretic learning (ITL) based penalty function is introduced to estimate a kernel-based similarity that maximizes the whole information potential variability. So, we seek for a reproducing kernel Hilbert space (RKHS) that spans the widest information force magnitudes among data points to support further clustering stages. Third, an entropy-like functional on positive definite matrices based on Renyi’s definition is adapted to develop a kernel-based representation approach which considers the statistical distribution and the salient data structures. Thereby, relevant input patterns are highlighted in unsupervised learning tasks. Particularly, the introduced approach is tested as a tool to encode relevant local and global input data relationships in dimensional reduction applications. Fourth, a supervised kernel-based representation is introduced via a metric learning procedure in RKHS that takes advantage of the user-prior knowledge, when available, regarding the studied learning task. Such an approach incorporates the proposed ITL-based kernel functional estimation strategy to adapt automatically the relevant representation using both the supervised information and the input data statistical distribution. As a result, relevant sample dependencies are highlighted by weighting the input features that mostly encode the supervised learning task. Finally, a new generalized kernel-based measure is proposed by taking advantage of different RKHSs. In this way, relevant dependencies are highlighted automatically by considering the input data domain-varying behavior and the user-prior knowledge (supervised information) when available. The proposed measure is an extension of the well-known crosscorrentropy function based on Hilbert space embeddings. Throughout the study, the proposed kernel-based framework is applied to biosignal and image data as an alternative to support aided diagnosis systems and image-based object analysis. Indeed, the introduced kernel-based framework improve, in most of the cases, unsupervised and supervised learning performances, aiding researchers in their quest to process and to favor the understanding of complex dataResumen: Hoy en día, el análisis de datos se ha convertido en un tema de gran interés para la comunidad científica, especialmente en campos como la automatización, el procesamiento de señales, el reconocimiento de patrones y el aprendizaje de máquina. En este sentido, la identificación, descripción, clasificación, visualización, y la agrupación de eventos o patrones son problemas importantes para desarrollos de ingeniería y cuestiones científicas, tales como: la biología, la medicina, la economía, la visión artificial, la inteligencia artificial y la producción industrial. No obstante, es difícil interpretar la información disponible debido a su complejidad y la gran cantidad de características obtenidas. Además, el análisis de los datos de entrada requiere del desarrollo de metodologías que permitan revelar los comportamientos relevantes del proceso estudiado, en particular, cuando tales señales contienen estructuras ocultas que varían sobre un dominio dado, por ejemplo, el espacio y/o el tiempo. Cuando la señal analizada contiene este tipo de propiedades, los rendimientos pueden ser inestables si se aplican directamente técnicas de procesamiento de señales y aprendizaje automático sin tener en cuenta la distribución estadística y la estructura de datos. Al respecto, las funciones núcleo (kernel) aparecen como un enfoque alternativo para abordar las limitantes antes mencionadas, proporcionando herramientas matemáticas flexibles que mejoran la representación de los datos de entrada. Por otra parte, los métodos basados en funciones núcleo son herramientas poderosas para el desarrollo de soluciones de mejor rendimiento mediante la adaptación del núcleo de acuerdo al problema en estudio. Sin embargo, la construcción de funciones núcleo apropiadas requieren del conocimiento previo por parte del usuario sobre los datos de entrada, el cual no está disponible en la mayoría de los casos prácticos. Por otra parte, a menudo la estimación de las funciones núcleo conllevan sesgos el modelo, siendo necesario apelar a simplificaciones matemáticas que no siempre son acordes con la realidad. En este estudio, se propone un marco de representación basado en métodos núcleo para resaltar relaciones relevantes entre los datos de forma automática en sistema de aprendizaje de máquina. A saber, el marco propuesto consta de cinco enfoques núcleo, en aras de adaptar la representación de acuerdo a las relaciones impuestas sobre las muestras y sobre el escenario de aprendizaje (sin/con supervisión). En primer lugar, se desarrolla un enfoque de representación núcleo que permite revelar las principales relaciones entre muestras de entrada mediante la inclusión de estructuras relevantes utilizando restricciones basadas en modelado por grafos. Por lo tanto, las estructuras de datos más sobresalientes se destacan con el objetivo de favorecer etapas posteriores de agrupamiento no supervisado. Este enfoque puede ser visto como una estrategia de depuración de grafos dentro de un marco de agrupamiento espectral que permite mejorar las consistencias locales y globales de los datos En segundo lugar, presentamos una metodología de representación núcleo que captura relaciones significativas entre muestras en términos de su distribución estadística. De este modo, se introduce una función de costo basada en aprendizaje por teoría de la información para estimar una similitud que maximice la variabilidad del potencial de información de los datos de entrada. Así, se busca un espacio de Hilbert generado por el núcleo que contenga altas fuerzas de información entre los puntos para favorecer el agrupamiento entre los mismos. En tercer lugar, se propone un esquema de representación que incluye un funcional de entropía para matrices definidas positivas a partir de la definición de Renyi. En este sentido, se pretenden incluir la distribución estadística de las muestras y sus estructuras relevantes. Por consiguiente, los patrones de entrada pertinentes se destacan en tareas de aprendizaje sin supervisión. En particular, el enfoque introducido se prueba como una herramienta para codificar las relaciones locales y globales de los datos en tareas de reducción de dimensión. En cuarto lugar, se introduce una metodología de representación núcleo supervisada a través de un aprendizaje de métrica en el espacio de Hilbert generado por una función núcleo en aras de aprovechar el conocimiento previo del usuario con respecto a la tarea de aprendizaje. Este enfoque incorpora un funcional por teoría de información que permite adaptar automáticamente la representación utilizando tanto información supervisada y la distribución estadística de los datos de entrada. Como resultado, las dependencias entre las muestras se resaltan mediante la ponderación de las características de entrada que codifican la tarea de aprendizaje supervisado. Por último, se propone una nueva medida núcleo mediante el aprovechamiento de diferentes espacios de representación. De este modo, las dependencias más relevantes entre las muestras se resaltan automáticamente considerando el dominio de interés de los datos de entrada y el conocimiento previo del usuario (información supervisada). La medida propuesta es una extensión de la función de cross-correntropia a partir de inmersiones en espacios de Hilbert. A lo largo del estudio, el esquema propuesto se valida sobre datos relacionados con bioseñales e imágenes como una alternativa para apoyar sistemas de apoyo diagnóstico y análisis objetivo basado en imágenes. De hecho, el marco introducido permite mejorar, en la mayoría de los casos, el rendimiento de sistemas de aprendizaje supervisado y no supervisado, favoreciendo la precisión de la tarea y la interpretabilidad de los datosDoctorad