Detection of anatomical structures in medical datasets

Detection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identification of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efficient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classifiers providing complementary information, the hybrid classifier provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classifiers are sufficiently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores