Abstract：Objective This paper proposed a brain joint segmentation and classification algorithm based
on proton density (ρ) and relaxation time (T1) and (T2), instead of the acquired gray level image. Methods
Estimation of proton density and relaxation time was made, then the approach exploited the statistical
distribution of the involved signals in the complex domain; at last a novel method for identifying the
optimal decision regions was proposed, which could achieve the ideal segmentation results. Results
Both simulated and real datasets were evaluated by using different methods. Qualitative analysis showed
that edges were well retrieved and small structures were preserved and completely clear. Quantitative
evaluation results showed that the proposed segmentation algorithm in this paper could provide the best
detection probability and false alarm probability. And it could acquire the maximal Dice coefficient and
Jaccard similarity indexes in case of different SNR (15～30 dB). Conclusion The proposed method based
on ρ, T1 and T2 maps was a feasible segmentation algorithm. And it could provide better robustness in the
noise environment, intensity inhomogeneity and clinical applications, which was of great value in clinical
popularization.
周啸虎，高伟，张子齐. 基于质子密度和弛豫时间的大脑MR图像分割新算法[J]. 中国医疗设备, 2016, 31(10): 25-28.
ZHOU Xiao-hu, GAO Wei,
ZHANG Zi-qi. A Novel Approach for Brain MR Image Segmentation Based on Proton
Density and Relaxation Time. China Medical Devices, 2016, 31(10): 25-28.
