Center of Modern Educational Technology, Mudanjiang Medical University, Mudanjiang, 157011 Heilongjiang, China.
Information Center Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, 157011 Heilongjiang, China.
Comput Math Methods Med. 2022 Mar 15;2022:4224749. doi: 10.1155/2022/4224749. eCollection 2022.
The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial ( < 0.05). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm ( < 0.05). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area ( < 0.05). FA of stage III+IV was smaller than those of stage I and II ( < 0.05), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II ( < 0.05). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.
本研究旨在分析基于模糊 C 均值（FCM）算法的 ARM-Linux 嵌入式系统在磁共振成像（MRI）图像中的应用，以预测脑肿瘤。提出了基于核函数的优化 FCM（OFCM）算法，并基于 ARM9 芯片和 Linux 记录器设计了 ARM-Linux 嵌入式成像系统，应用于脑肿瘤患者的 MRI 图像。结果发现，OFCM 算法的灵敏度、特异性和准确率（90.46%、88.97%和 97.46%）明显高于确定性 C 均值聚类算法（80.38%、77.98%和 85.24%）和传统 FCM 算法（83.26%、79.56%和 86.45%），差异具有统计学意义（<0.05）。与确定性 C 均值聚类算法和传统 FCM 算法相比，OFCM 算法的均方误差（ME）和运行时间明显降低（<0.05）。肿瘤实质区、周围水肿区和正常白质区的各向异性分数（FA）和平均扩散系数（MD）差异有统计学意义（<0.05）。III+IV 期的 FA 小于 I+II 期（<0.05），而 III+IV 期的 ADC 值大于 I+II 期（<0.05）。总之，通过 OFCM 解决了 FCM 中数据更新和数据聚类效率低的问题。此外，还提高了 ARM-Linux 嵌入式成像系统的计算效率，从而通过基于自适应 FCM 增量聚类算法的 ARM-Linux 嵌入式系统更好地实现了脑肿瘤患者的预测。
