Procesado de imágenes médicas en MATLAB

La relación entre la ingeniería y la medicina cada vez se está haciendo más estrecha, y debido a esto se ha creado una nueva disciplina, la bioingeniería, ámbito en el que se centra el proyecto. Este ámbito cobra gran interés debido al rápido desarrollo de nuevas tecnologías que en particular permiten, facilitan y mejoran la obtención de diagnósticos médicos respecto de los métodos tradicionales. Dentro de la bioingeniería, el campo que está teniendo mayor desarrollo es el de la imagen médica, gracias al cual se pueden obtener imágenes del interior del cuerpo humano con métodos no invasivos y sin necesidad de recurrir a la cirugía. Mediante métodos como la resonancia magnética, rayos X, medicina nuclear o ultrasonidos, se pueden obtener imágenes del cuerpo humano para realizar diagnósticos. Para que esas imágenes puedan ser utilizadas con ese fin hay que realizar un correcto tratamiento de éstas mediante técnicas de procesado digital. En ése ámbito del procesado digital de las imágenes médicas es en el que se ha realizado este proyecto. Gracias al desarrollo del tratamiento digital de imágenes con métodos de extracción de información, mejora de la visualización o resaltado de rasgos de interés de las imágenes, se puede facilitar y mejorar el diagnóstico de los especialistas. Por todo esto en una época en la que se quieren automatizar todos los procesos para mejorar la eficacia del trabajo realizado, el automatizar el procesado de las imágenes para extraer información con mayor facilidad, es muy útil. Actualmente una de las herramientas más potentes en el tratamiento de imágenes médicas es Matlab, gracias a su toolbox de procesado de imágenes. Por ello se eligió este software para el desarrollo de la parte práctica de este proyecto, su potencia y versatilidad simplifican la implementación de algoritmos. Este proyecto se estructura en dos partes. En la primera se realiza una descripción general de las diferentes modalidades de obtención de imágenes médicas y se explican los diferentes usos de cada método, dependiendo del campo de aplicación. Posteriormente se hace una descripción de las técnicas más importantes de procesado de imagen digital que han sido utilizadas en el proyecto. En la segunda parte se desarrollan cuatro aplicaciones en Matlab para ejemplificar el desarrollo de algoritmos de procesado de imágenes médicas. Dichas implementaciones demuestran la aplicación y utilidad de los conceptos explicados anteriormente en la parte teórica, como la segmentación y operaciones de filtrado espacial de la imagen, así como otros conceptos específicos. Las aplicaciones ejemplo desarrolladas han sido: obtención del porcentaje de metástasis de un tejido, diagnóstico de las deformidades de la columna vertebral, obtención de la MTF de una cámara de rayos gamma y medida del área de un fibroadenoma de una ecografía de mama. Por último, para cada una de las aplicaciones se detallará su utilidad en el campo de la imagen médica, los resultados obtenidos y su implementación en una interfaz gráfica para facilitar su uso. ABSTRACT. The relationship between medicine and engineering is becoming closer than ever giving birth to a recently appeared science field: bioengineering. This project is focused on this subject. This recent field is becoming more and more important due to the fast development of new technologies that provide tools to improve disease diagnosis, with regard to traditional procedures. In bioengineering the fastest growing field is medical imaging, in which we can obtain images of the inside of the human body without need of surgery. Nowadays by means of the medical modalities of magnetic resonance, X ray, nuclear medicine or ultrasound, we can obtain images to make a more accurate diagnosis. For those images to be useful within the medical field, they should be processed properly with some digital image processing techniques. It is in this field of digital medical image processing where this project is developed. Thanks to the development of digital image processing providing methods for data collection, improved visualization or data highlighting, diagnosis can be eased and facilitated. In an age where automation of processes is much sought, automated digital image processing to ease data collection is extremely useful. One of the most powerful image processing tools is Matlab, together with its image processing toolbox. That is the reason why that software was chosen to develop the practical algorithms in this project. This final project is divided into two main parts. Firstly, the different modalities for obtaining medical images will be described. The different usages of each method according to the application will also be specified. Afterwards we will give a brief description of the most important image processing tools that have been used in the project. Secondly, four algorithms in Matlab are implemented, to provide practical examples of medical image processing algorithms. This implementation shows the usefulness of the concepts previously explained in the first part, such as: segmentation or spatial filtering. The particular applications examples that have been developed are: calculation of the metastasis percentage of a tissue, diagnosis of spinal deformity, approximation to the MTF of a gamma camera, and measurement of the area of a fibroadenoma in an ultrasound image. Finally, for each of the applications developed, we will detail its usefulness within the medical field, the results obtained, and its implementation in a graphical user interface to ensure ease of use.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differenti- ated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specif- ically, the direct relations between brain and spinal cord tis- sue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic- based damage criteria, simulated function-based damage cri- teria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus provid- ing a link between mechanical trauma and subsequent func- tional deficits.

A data-globe and immersive virtual reality environment for upper limb rehabilitation after spinal cord injury

While a number of virtual data-gloves have been used in stroke, there is little evidence about their use in spinal cord injury (SCI). A pilot clinical experience with nine SCI subjects was performed comparing two groups: one carried out a virtual rehabilitation training based on the use of a data glove, CyberTouch combined with traditional rehabilitation, during 30 minutes a day twice a week along two weeks; while the other made only conventional rehabilitation. Furthermore, two functional indexes were developed in order to assess the patient’s performance of the sessions: normalized trajectory lengths and repeatability. While differences between groups were not statistically significant, the data-glove group seemed to obtain better results in the muscle balance and functional parameters, and in the dexterity, coordination and fine grip tests. Related to the indexes that we implemented, normalized trajectory lengths and repeatability, every patient showed an improvement in at least one of the indexes, either along Y-axis trajectory or Z-axis trajectory. This study might be a step in investigating new ways of treatments and objective measures in order to obtain more accurate data about the patient’s evolution, allowing the clinicians to develop rehabilitation treatments, adapted to the abilities and needs of the patients.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differentiated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specifically, the direct relations between brain and spinal cord tissue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic-based damage criteria, simulated function-based damage criteria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus providing a link between mechanical trauma and subsequent functional deficits