Image processing in medicine advances for phenotype characterization, computer-assisted diagnosis and surgical planning

En esta Tesis presentamos nuestras contribuciones al estado del arte en procesamiento digital de imágenes médicas, articulando nuestra exposición en torno a los tres principales objetivos de la adquisición de imágenes en medicina: la prevención, el diagnóstico y el tratamiento de las enfermedades. La prevención de la enfermedad se puede conseguir a veces mediante una caracterización cuidadosa de los fenotipos propios de la misma. Tal caracterización a menudo se alcanza a partir de imágenes. Presentamos nuestro trabajo en caracterización del enfisema pulmonar a partir de imágenes TAC (Tomografía Axial Computerizada) de tórax en alta resolución, a través del análisis de las texturas locales de la imagen. Nos proponemos llenar el vacío existente entre la práctica clínica actual, y las sofisticadas pero costosas técnicas de caracterización de regiones texturadas, disponibles en la literatura. Lo hacemos utilizando la distribución local de intensidades como un descriptor adecuado para determinar el grado de destrucción de tejido en pulmones enfisematosos. Se presentan interesantes resultados derivados del análisis de varios cientos de imágenes para niveles variables de severidad de la enfermedad, sugiriendo tanto la validez de nuestras hipótesis, como la pertinencia de este tipo de análisis para la comprensión de la enfermedad pulmonar obstructiva crónica. El procesado de imágenes médicas también puede asistir en el diagnóstico y detección de enfermedades. Presentamos nuestras contribuciones a este campo, que consisten en técnicas de segmentación y cuantificación de imágenes dermatoscópicas de lesiones de la piel. La segmentación se obtiene mediante un novedoso algoritmo basado en contornos activos que explota al máximo el contenido cromático de las imágenes, gracias a la maximización de la discrepancia mediante comparaciones cross-bin. La cuantificación de texturas en lesiones melanocíticas se lleva a cabo utilizando un modelado de los patrones de pigmentación basado en campos aleatorios de Markov, en un esfuerzo por adoptar la tendencia emergente en dermatología: la detección de la malignidad mediante el análisis de la irregularidad de la textura. Los resultados para ambas técnicas son validados con un conjunto significativo de imágenes dermatológicas, sugiriendo líneas interesantes para la detección automática del melanoma maligno. Cuando la enfermedad ya está presente, el tratamiento digital de imágenes puede asistir en la planificación quirúrgica y la intervención guiada por imagen. La planificación terapeútica, ejemplicada por la planificación de cirugía plástica usando realidad virtual, se aborda en nuestro trabajo en segmentación de hueso/grasa/músculo en imágenes TAC. Usando un abordaje interactivo e incremental, nuestro sistema permite obtener segmentaciones precisas a partir de unos cuantos clics de ratón para una gran variedad de condiciones de adquisición y frente a anatomícas anormales. Presentamos nuestra metodología, y nuestra validación experimental profusa basada tanto en segmentaciones manuales como en valoraciones subjetivas de los usuarios, e indicamos referencias al lector que detallan los beneficios obtenidos con el uso de la plataforma de planifificación que utiliza nuestro algoritmo. Como conclusión presentamos una disertación final sobre la importancia de nuestros resultados y las líneas probables de trabajo futuro hacía el objetivo último de mejorar el cuidado de la salud mediante técnicas de tratamiento digital de imágenes médicas.In this Thesis we present our contributions to the state-of-the-art in medical image processing, articulating our exposition around the three main roles of medical imaging: disease prevention, diagnosis and treatment. Disease prevention can sometimes be achieved by proper characterization of disease phenotypes. Such characterization is often attained from the standpoint of imaging. We present our work in characterization of emphysema from highresolution computed-tomography images via quanti_cation of local texture. We propose to _ll the gap between current clinical practice and sophisticated texture approaches by the use of local intensity distributions as an adequate descriptor for the degree of tissue destruction in the emphysematous lung. Interesting results are presented from the analysis of several hundred datasets of lung CT for varying disease severity, suggesting both the correctness of our hypotheses and the pertinence of _ne emphysema quanti_cation for understanding of chronic obstructive pulmonary disease. Medical image processing can also assist in the diagnosis and detection of disease. We introduce our contributions to this_eld, consisting of segmentation and quanti_cation techniques in application to dermatoscopy images of skin lesions. Segmentation is achieved via a novel active contour algorithm that fully exploits the color content of the images, via cross-bin histogram dissimilarity maximization. Texture quanti_cation in the context of melanocytic lesions is performed using modelization of the pigmentation patterns via Markov random elds, in an e_ort to embrace the emerging trend in dermatology: malignancy assessment based on texture irregularity analysis. Experimental results for both, the segmentation and quanti_cation proposed techniques, will be validated on a signi_cant set of dermatoscopy images, suggesting interesting pathways towards automatic detection and diagnosis of malignant melanoma. Once disease has occurred, image processing can assist in therapeutical planning and image-guided intervention. Therapeutical planning, exempli_ed by virtual reality surgical planning, is tackled by our work in segmentation of bone/fat/muscle in CT images for plastic surgery planning. Using an interactive, incremental approach, our system is able to provide accurate segmentations based on a couple of mouse-clicks for a wide variety of imaging conditions and abnormal anatomies. We present our methodology, and provide profuse experimental validation based on manual segmentations and subjective assessment, and refer the reader to related work reporting on the clinical bene_ts obtained using the virtual reality platform hosting our algorithm. As a conclusion we present a _nal dissertation on the signi_cance of our results and the probable lines of future work towards fully bene_tting healthcare using medical image processing

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higher-dimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging

Non-rigid medical image registration with extended free form deformations: modelling general tissue transitions

Image registration seeks pointwise correspondences between the same or analogous objects in different images. Conventional registration methods generally impose continuity and smoothness throughout the image. However, there are cases in which the deformations may involve discontinuities. In general, the discontinuities can be of different types, depending on the physical properties of the tissue transitions involved and boundary conditions. For instance, in the respiratory motion the lungs slide along the thoracic cage following the tangential direction of their interface. In the normal direction, however, the lungs and the thoracic cage are constrained to be always in contact but they have different material properties producing different compression or expansion rates. In the literature, there is no generic method, which handles different types of discontinuities and considers their directional dependence. The aim of this thesis is to develop a general registration framework that is able to correctly model different types of tissue transitions with a general formalism. This has led to the development of the eXtended Free Form Deformation (XFFD) registration method. XFFD borrows the concept of the interpolation method from the eXtended Finite Element method (XFEM) to incorporate discontinuities by enriching B-spline basis functions, coupled with extra degrees of freedom. XFFD can handle different types of discontinuities and encodes their directional-dependence without any additional constraints. XFFD has been evaluated on digital phantoms, publicly available 3D liver and lung CT images. The experiments show that XFFD improves on previous methods and that it is important to employ the correct model that corresponds to the discontinuity type involved at the tissue transition. The effect of using incorrect models is more evident in the strain, which measures mechanical properties of the tissues