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Hippocampal Segmentation using Mean Shift Algorithm

机译:基于均值漂移算法的海马分割

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The population is aging as the years pass. There is an increase in life expectancy, but also a decrease in the quality of life for the presence of chronic degenerative diseases. Processing medical images can identify brain changes typical of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) at an early stage. We propose a new method of segmentation technique using Mean Shift algorithm applying probabilistic maps and Support Vector Machine with Linear and Radial Basis Kernel for segmentation of the hippocampus on Magnetic Resonance Images (MRI). The similarity index of DICE for a 8 control subject was calculated obtaining a mean value of 0.7053 ± 0.0996 using Linear kernel and 0.7275 ± 0.1335 using RBF kernel compared with the manual segmentation made by radiologist.
机译:随着岁月的流逝,人口正在老龄化。对于存在慢性退行性疾病,预期寿命会增加,但生活质量也会下降。处理医学图像可以在早期识别出典型的阿尔茨海默氏病(AD)和轻度认知障碍(MCI)的大脑变化。我们提出了一种应用均值漂移算法的新方法,该方法应用概率图和具有线性和径向基核的支持向量机对磁共振图像(MRI)进行海马分割。与放射线科医生进行的手动分割相比,计算了8个对照受试者的DICE相似性指数,使用线性核的平均值为0.7053±0.0996,使用RBF核的平均值为0.7275±0.1335。

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