Intracranial hemorrhage area segmentation method based on three-dimensional super voxel and system thereof

Abstract

The invention discloses an intracranial hemorrhage area segmentation method based on three-dimensional super voxel and a system thereof. The intracranial hemorrhage area segmentation method is characterized in that a CT image pre-processing phase and an intracranial hemorrhage area segmentation phase based on the three-dimensional super voxel can be provided; according to the CT image pre-processing phase, the format conversion of the two-dimensional CT image sequence can be carried out, the skull structure can be extracted, and the intracranial area can be found; according to the intracranial hemorrhage area segmentation phase, the two-dimensional local CT image can be reconstructed on the three-dimensional space, and the three-dimensional image can be divided into the super voxels having the similar sizes by adopting the super voxel algorithm, and the super voxels can be divided into the foreground part and the background part by adopting the graph cut algorithm. The intracranial structure can be extracted by adopting the pre-processing, and the segmentation can be refined step by step, and the super voxels can be used for the calculation by replacing the pixels, and then the hemorrhage area detection accuracy can be effectively improved. The method and the system provided by the invention are advantageous in that the hemorrhage areas having different reasons, different positions, and different sizes can be effectively detected, and the important function can be provided for the computer-aided medical application in the clinic.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a three-dimensional supervoxel-based intracranial hemorrhage region segmentation method and system. Background technique [0002] Intracranial hemorrhage (ICH) is one of the most serious acute cerebrovascular diseases, and it is also an important cause of acute neurological disorders such as hemiplegia. Therefore, for clinical treatment, early diagnosis of intracranial hemorrhage is of great significance. Compared with clinical manifestations, computed tomography (CT) scan and magnetic resonance imaging (MRI) scan, which can be detected without trauma, can more directly and accurately reflect the severity and evolution trend of intracranial hemorrhage. At the same time, because the cost of CT detection is much less than that of MRI detection, most patients will choose CT detection. Fresh hematomas usually appear as hyperluminous areas with indistinct borders on CT images...

AI Technical Summary

Problems solved by technology

First, most of these methods use very simple segmentation algorithms, such as clustering and thresholding, etc., and while these methods may perform well in natural image processing, they do not perform well in complex situations, such as bleeding regions overlapping brain tissue. These methods do not work well when the edges of the bleed or bleed are not sufficiently discriminative

Second, most of the existing algorithms are only suitable for processing two-dimensional images

However, CT imaging is a three-dimensional process, so a series of parallel scanning image frames will be generated, while the 2D segmentation algorithm will ignore some important inter-frame information

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