KEYWORDS: Medical imaging, Image segmentation, Image processing, Databases, Data acquisition, Data storage, Feature extraction, Image storage, Image analysis, Image processing algorithms and systems
Due to the large volume and density of the medical images data, it is necessary the use of suitable database systems to facilitate their storage and management, interacting with the PACS (Picture Archiving and Communication Systems). This paper presents an architecture designed for acquisition and storage of the extracted data related to medical images, emphasizing the importance of experts in acquisition of consistent data. This work also presents the division of the information contained in the medical images into levels such as: low level, segmentation level, interpretation level, semantic level and related information. The levels work as a basis to the database schema represented by ER (entity relationship). This architecture has been validated by a content-based image retrieval system for Neonatology support.
The techniques for fusion of satellite images with different spatial resolutions aims to enhance the image quality, that allows a better visual interpretation. Ideally, the resulting image must keep the spectral resolution, leading to a more precise image segmentation and classification. Many different methods have been proposed to perform image fusion for medium resolution images (e.g., Landsat TM and SPOT). The launching of IKONOS satellite became possible the obtaining of high spatial resolution images (1 meter in panchromatic mode). These images have spatial information for mapping applications and analysis of urban areas. However, the multispectral images, that provide the most relevant information for thematic applications, are obtained with spatial resolution of 4 meters. This work compares the experimental results of 5 traditional methods (Band Substitution, IHS Transformation, HSV Transformation, Principal Component Substitution and High-Pass Filtering) applied to fusion of multispectral and panchromatic images of IKONOS, and evaluates the applicability of these methods for high resolution images. The analysis of the results are done by: 1) visual inspection, 2) statistical comparison by correlation coefficient, and 3) classification of the resulting image. The test area corresponds to an urban region with different types of land cover.
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