The task of reef restoration is very challenging for volunteer SCUBA divers, if it has to be carried out at deep sea, 200 meters, and low temperatures. This kind of task can be properly performed by an Autonomous Underwater Vehicle (AUV); able to detect the location of reef areas and approach them. The aim of this study is the development of a vision system for coral detections based on supervised machine learning. In order to achieve this, we use a bank of Gabor Wavelet filters to extract texture feature descriptors, we use learning classifiers, from OpenCV library, to discriminate coral from non-coral reef. We compare: running time, accuracy, specificity and sensitivity of nine different learning classifiers. We select Decision Trees algorithm because it shows the fastest and the most accurate performance. For the evaluation of this system, we use a database of 621 images (developed for this purpose), that represents the coral reef located in Belize: 110 for training the classifiers and 511 for testing the coral detector.
Resumen-El objetivo principal de este trabajo es diseñar e implementar un software libre para leer historias clínicas electrónicas basadas en el estándar Health Level 7 / Clinical Document Architecture (HL7/CDA). La implementación del software fue realizada utilizando la metodología Programación Extrema, PHP, Javascript y XML asíncronos (AJAX) y las herramientas Apache 2 y Eclipse 3.1. El lector recibe un documento XML codificado en HL7/CDA y luego organiza su contenido en la memoria del computador. Las pruebas fueron realizadas en Linux Ubuntu 6.06 LTS y Windows XP SP2, utilizando Apache 2 y PHP 5. Los resultados obtenidos muestran que es posible la recepción de historias clínicas electrónicas que se encuentren en conformidad con el estándar HL7/CDA. Se concluye que el lector facilita la gestión de la información contenida en historias clínicas codificadas en HL7/CDA luego de su recepción, representando así una contribución al intercambio de información clínica para servicios de telemedicina.
DICOMDIR directory files are useful in medical software applications because they allow organized access to images and information sets that come from radiological studies that are stored in conformance with the digital imaging and communication in medicine (DICOM) standard. During the medical application software development, specialized programming libraries are commonly used in order to solve the requirements of computation and scientific visualization. However, these libraries do not provide suitable tools for reading DICOMDIR files, making necessary the implementation of a flexible tool for reading these files, which can be also easily integrated into applications under development. To solve this problem, this work introduces an object-oriented design and an open-source implementation for such reading tool. It produces an output data tree containing the information of the DICOM images and their related radiological studies, which can be browsed easily in a structured way through navigation interfaces coupled to it.
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