Feature Extraction and Research Based on MRI Image of Cerebral Hemorrhage
Xiaoyi Ruan, Tao Shi, Hua Chen, Huimin Yao · 2020
MRI is a kind of biological magnetic spin imaging technology. Making full use of MRI images is of great significance for improving the accuracy of medical image diagnosis process for the diagnosis of diseases. In the diagnosis of cerebral hemorrhage, the location and size of the hemorrhage area are meaningful indicators for diagnosing the severity of cerebral hemorrhage. In order to assist the doctor's diagnosis, we have proposed an automatic detection method based on the combination of morphology and threshold segmentation to calculate the location and size of the bleeding point according to the characteristics of the image of different sequences in the magnetic resonance image. : (1) Pre-process the MRI image; (2) Remove the skull part by morphology and leave only the brain soft tissue image; (3) Use Oust and morphology to extract the suspected lesion parts in the brain image; (4) Finally, The longitudinal relaxation time (T1 image) and transverse relaxation time (T2 images) were fused to locate the bleeding point. The experimental results show that the algorithm has high feasibility and accurate positioning of bleeding points.MRI is a kind of biological magnetic spin imaging technology. Making full use of MRI images is of great significance for improving the accuracy of medical image diagnosis process for the diagnosis of diseases. In the diagnosis of cerebral hemorrhage, the location and size of the hemorrhage area are meaningful indicators for diagnosing the severity of cerebral hemorrhage. In order to assist the doctor's diagnosis, we have proposed an automatic detection method based on the combination of morphology and threshold segmentation to calculate the location and size of the bleeding point according to the characteristics of the image of different sequences in the magnetic resonance image. : (1) Pre-process the MRI image; (2) Remove the skull part by morphology and leave only the brain soft tissue image; (3) Use Oust and morphology to extract the suspected lesion parts in the brain image; (4) Finally, The longitudinal relaxation time (T1 image) and transverse relaxation time (T2 images) were fused to locate the bleeding point. The experimental results show that the algorithm has high feasibility and accurate positioning of bleeding points.