Automatic dividing method for cerebral ischemia focus area

An automatic segmentation and lesion area technology, applied in the field of image processing, can solve the problem that the segmentation result depends on the initial segmentation, is not suitable for lesion area segmentation, and does not consider the local volume effect, and achieves the effect of overcoming noise.
CN1632830AInactive Publication Date: 2005-06-29INST OF AUTOMATION CHINESE ACAD OF SCI
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
INST OF AUTOMATION CHINESE ACAD OF SCI
Publication Date
2005-06-29
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

This invention relates to image process technique, and especially to an automatic division of brain blood shortage area based on multiple-size statistic sorting and local container sorting method, which comprises the following steps: to valuate the DTI image spreading value and direction isomerism; second to computer the size space; third to sort the multiple size; fourth to sort the local container. This invention is of high application value in medical assistant dialogue system, medical image three-dimensional recreation system and clinic disease qualitatively diagnose analysis.
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Description

technical field

[0001] The invention relates to image processing technology, in particular to an automatic segmentation method for cerebral ischemic lesion areas based on multi-scale statistical classification and local volume classification methods. Background technique

[0002] The so-called image segmentation refers to distinguishing different regions with special meaning in the image, these regions do not cross each other, and each region satisfies the consistency of a specific region. From the perspective of processing objects, segmentation is to determine the location of the target of interest in the image matrix. Obviously, only by extracting the "target object of interest" from the complex scene, can further quantitative analysis or identification of each sub-region be possible, and then the image can be understood. Image segmentation includes methods such as threshold segmentation, edge detection, and statistical classification. The features available for image se...

Claims

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