01-03-2013, 01:05 PM
Automatic Segmentation of Digital Images Applied in Cardiac Medical Images
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INTRODUCTION
The biological vision system is one of the most important means of exploration of the world to the humans, performing complex tasks with great ease such as analysis, interpretation, recognition and pattern classification. For this reason many studies attempt to produce artificial vision systems with the same efficiency of the biological system. This task is still highly complex, mainly to implement one of the most obvious problem, the quantification and qualification of information’s represented in many different fields, such as intensity of gray level, edges, contours and texture. These attributes are naturally sought by the human visual system when the measured signal is an image [1]. One possibility to represent an artificial vision system efficient is to use appropriate methods of segmentation, considered as a first step for analyzing an image, it allows to separate the objects in parts, according to some criterion of uniformity [2]. For high quality segmentation systems, digital image processing is used in a primary stage of thresholding to separate the object of the rest of the image. The thresholding consist in to identify in an image, a threshold of intensity in which the object distinguish better threshold takes a subjective criterion of a human operator. Several methods had been proposed to do this automatically based on different criteria in the image, such as those proposed [3], [4], [5], [6], [7] e [8]. However, in many cases is not achieved a threshold that provides a good segmentation of the entire image. For such situations are applied techniques of variables and multilevel thresholding based on analytical studies, using the parameters of statistical distribution of gray levels, or graphics, using the histogram display the gray level image.
EXISTING TECHNOLOGIES
The segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific.
Image segmentation is the process by which individual image pixels are grouped into partitions, according to some intrinsic properties of the image, e.g., grey levels, contrast, spectral values or textural properties. The selection of a segmentation technique depends greatly on the type of data being analyzed and on the application area. The framework of our study is the creation of segmented images from remotely sensed multi spectral image data.
CONCLUSIONS
We have presented a parallel approach that integrates region growing and edge detection at the symbol-level, by pruning the binary tree representation of region growing results based on the edges produced by a Canny edge detector. The Hausdorff distance metric was used to compare the edges detected by the Canny edge detector with the region boundaries produced by region growing. All the algorithms chosen in this method allow for implementation on a massively parallel processor, making this technique very valuable for the processing of future large amounts of remotely sensed data.