Genetic learning for adaptive image segmentation / Bir Bhanu, Sungkee Lee. p. cm. --(The Kluwer international series in engineering and computer science ; 287. Robotics) Includes bibliographical references and index. ISBN 978-1-4613-6198-5 ISBN 978-1-4615-2774-9 (eBook) 10 SUMMARY REFERENCES INDEX vii 218 220 255 261 269 Preface xixThis book also explores a hybrid search scheme that combines genetic algorithms and hill climbing for adaptive image segmentation. It provides experimental results and compares its performance and efficiency with that of the baseline approach that uses only the genetic algorithm.The book further develops the baseline adaptive image segmentation system for multiobjective optimization. The global and local quality measures are optimized simultaneously for adaptive image segmentation. Experimental results are provided and compared with the baseline approach.The adaptive segmentation system presented in this book is very fundamental in nature and is not dependent on any specific segmentation algorithm or sensor data (visible, infrared, laser, etc.).Zucker [70] summarizes the above conditions as follows. The first condition implies that the segmentation is complete: every pixel is in a region. This means that the segmentation algorithm should not terminate until every pixel in an image is processed. The second condition requires that regions are connected, i.e., regions are composed of contiguous pixels. The third condition determines what kind of properties the segmented regions should have. It is either a syntactic image characteristic (e.g., intensity, color, texture) or corresponds to some semantic interpretation. The fourth condition expresses the maximality of each region in the segmentation.This list of conditions for an image segmentation does not lead to a unique segmentation algorithm, but does suggest important aspects of such algorithms. CHARACTERISTICS OF THE IMAGE SEGMENTATION PROBLEMImage segmentation is typically the first and most diflicult task of any automated image understanding process. It is also known as an ill-defined problem. It refers to the grouping of parts of an image that have "similar" image characterist(cs. All subsequent interpretation tasks, including o~iect detection, feature extraction, object recognition, and classification, rely heavily on the quality of the segmentation results.Despite the large number of segmentation techniques presently available [5,27,35], no general methods have been found that perform adequately across a diverse set of images. There are many reasons why we do not have a general-purpose image segmentation system that will work for all images:First, a two-dimensional image represents potentially an infinite number of scenes. For instance, an image of 128 by 128 pixels in size and 8 bits in pixel resolution can represent, (2 8 )128xI2R == 1039457 different scenes. For such a variety of scenes, it is impossible to define a single logical predicate that produces good segmentation for each possible image.Second, images of natural scenes...
ObjectivesIncreasing use of medical devices outside of healthcare facilities inevitably requires connectivity and interoperability between medical devices and healthcare information systems. To this end, standards have been developed and used to provide interoperability between personal health devices (PHDs) and external systems. ISO/IEEE 11073 standards and IHE PCD-01 standard messages have been used the most in the exchange of observation data of health devices. Recently, transmitting observation data using the HL7 FHIR standard has been devised in the name of DoF (Devices on FHIR) and adopted very fast. We compare and analyze these standards and suggest that which standard will work best at the different environments of device usage.MethodsWe generated each message/resource of the three standards for observed vital signs from blood pressure monitor and thermometer. Then, the size, the contents, and the exchange processes of these messages are compared and analyzed.ResultsISO/IEEE 11073 standard message has the smallest data size, but it has no ability to contain the key information, patient information. On the other hand, PCD-01 messages and FHIR standards have the fields for patient information. HL7 DoF standards provide reusing of information unit known as resource, and it is relatively easy to parse DoF messages since it uses widely known XML and JSON.ConclusionsISO/IEEE 11073 standards are suitable for devices having very small computing power. IHE PCD-01 and HL7 DoF messages can be used for the devices that need to be connected to hospital information systems that require patient information. When information reuse is frequent, DoF is advantageous over PCD-01.
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