The success of stereotactic surgery for Deep Brain Stimulation depends critically on the exact positioning of a microelectrode recording in a target area of the brain. This paper presents the software system NEUROZONE composed of two main applications: first, it allows online recognition of brain structures by the analysis of signals from microelectrode recordings (MER), and second, it processes and analyses off-line databases allowing the inclusion of new trained classifiers for automatic identification. The software serves as a support to the analysis done by a medical specialist during surgery, and seeks to reduce the adverse side effects that may occur because of inadequate identification of the target areas. The software also allows the specialists to label recordings obtained during surgery, in order to generate a new off-line database or increase the amount of records in an already existing off-line database. NEUROZONE has been tested for Deep Brain Stimulation performed at the Institute for Epilepsy and Parkinson of the Eje Cafetero (Colombia), achieving positive identifications of the Subthalamic Nucleus (STN) over to 85% using a naive Bayes classifier.
Abstract-Deep brain stimulation (DBS) is a surgical procedure used to treat Parkinson disease, the success of this surgery relies in the correct location of the neurostimulator device. A 3D Brain atlas allows to interactively visualizing the brain structures over a 3D medical image volume giving a view over the real area of surgery where the neurostimulator device has to be allocated. The movement of the 3D image volume is commonly made by a computer mouse or touch screens, which are not the best for a sterile environment as a surgery room and have some limitations as distance and move sensibility of the computer cursor. Hence in this paper we proposed a Human-Computer Interaction (HCI) to navigate over the 3D brain atlas structure and improve the limitations that the common computer peripherals have. For the development of the HCI was used the bluetooth sensor MYO of ThalamicLabs TM . The HCI developed were tested in the planning of DBS surgery under the NEURONAV software [1] giving a suitable navigation and control over 3D atlas.
RESUMENEl registro es una de las tareas más importantes dentro del procesamiento de imágenes, consiste en encontrar una transformación geométrica que relacione los puntos de una imagen y otra con el fin de corregir las diferencias de tamaño, posición, orientación o distorsión espacial que pueda presentarse, de manera que sea posible una comparación directa entre ellas que facilite el análisis por parte del especialista, ya sea para el diagnóstico de una enfermedad o para la planeación de una cirugía extereotáxica. En los últimos años se han propuesto técnicas de registro multimodal, especialmente entre imágenes CT (Tomografía computarizada) y MRI (Resonancia magnética), sin embargo, algunos de los problemas que aún se presentan permanecen sin resolver, y por ello son un campo activo de investigación. Debido a los problemas de exactitud que presentan los modelos de registro rígidos y no rígidos unimodales, los cuales aún no alcanzan la precisión necesaria para aplicaciones de neurocirugía y la necesidad de contar con herramientas de neuronavegación para el apoyo en la planeamiento de cirugías derivadas de procesos de neurocirugía, este artículo propone el desarrollo de una herramienta para el registro de estructuras cerebrales 3d utilizando técnicas de procesamiento de imágenes. Los resultados evidencian que una herramienta de registro permite emparejar un determinado atlas cerebral a un registro histológico de un determinado paciente, además que del sistema desarrollado permite su ejecución en línea lo cual beneficia la funcionalidad del sistema en procesos de neurocirugía. ABSTRACTMedical image registration is one of the most important tasks in image guide surgery. It consists of finding a geometrical transformation that relates the points of images in order to correct for differences in size, position, orientation or spatial distortion that can occur, so it is possible a direct comparison between them to facilitate analysis by the specialist, either for diagnosis of a disease or for planning a stereotaxic surgery. In recent years multimodal registration techniques have been proposed, especially among images CT (computed tomography) and MRI (Magnetic Resonance Imaging). However, some problems still remain unsolved, and are therefore an active field of research. Due to problems of accuracy in models of rigid and non-rigid registration , which have not yet reached the precision required for neurosurgical applications, this article proposes the development of a tool for 3D brain registration using image processing techniques . The results show that a registration tool allows matching a given brain histological atlas of a particular patient. Besides, the developed system can be executed online, which benefits system functionality in neurosurgery procedures.
La fatiga al conducir es uno de las principales causas de accidentes de tránsito. Los sistemas de detección de fatiga basados en visión por computador tienen gran potencial dada su característica de no-invasividad. Los principales desafíos que se presentan son los movimientos rápidos de ojos y boca, cambios de pose y variacio-nes de iluminación. En este trabajo se entrena un Modelo de Forma Activa para la detección de características faciales extraídas del modelo paramétrico Candide-3. Se describe la metodología de caracterización a partir del modelo paramétrico. Se evalúan de forma cuantitativa la exactitud en: la detección de características y la estimación de los parámetros asociados a la fatiga; analizando su robustez a variaciones de pose y variaciones locales de las regiones de interés. El modelo utilizado y la metodología de caracterización mostraron eficiencia para detectar la fatiga en el 100% de los casos evaluados.
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