Department of Pediatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, Sichuan, China.
J Healthc Eng. 2021 Jul 7;2021:5678994. doi: 10.1155/2021/5678994. eCollection 2021.
In order to provide theoretical support for clinical diagnosis, the diagnostic value of the optimized fuzzy C-means (FCM) algorithm combined with coronal magnetic resonance imaging (MRI) scan was investigated in the diagnosis of tracheal foreign bodies in children. The anisotropic filtering was applied to optimize the traditional FCM algorithm, so as to construct a new MRI image segmentation algorithm, namely, AFFCM algorithm. Then, the traditional FCM algorithm, the FCM algorithm based on the kernel function (KFCM), and the FCM algorithm based on the spatial neighborhood information (RFCM) were introduced for comparison with the AFFCM. 28 children diagnosed with foreign bodies in the trachea were selected for MRI diagnosis, and AFFCM was used for segmentation. The partition coefficient, segmentation entropy, and the correlation degree between classes after fuzzy division of the four algorithms were recorded, and the location and distribution of foreign bodies in the trachea and the types of foreign bodies were also collected. Besides, the MRI scanning and chest X-rays of the children with foreign bodies in the trachea should also be recorded in terms of the positive rate, diagnosis rate, and indirect signs. The class division coefficient and interclass correlation degree after fuzzy division of AFFCM were markedly greater than those of FCM, KFCM, and RFCM ( < 0.05), while the segmentation entropy of AFFCM was less sharp than the entropies of FCM, KFCM, and RFCM ( < 0.05). Among the 28 children, there were 5 cases with foreign bodies in the trachea (17.86%), 10 cases in the left bronchus (35.71%), and 13 cases in the right bronchus (46.43%). Among the foreign body types, there were 10 cases of melon seeds (35.71%), 6 cases of peanuts (21.43%), and 5 cases of beans (17.86%). The positive rate (89.29%) and diagnosis rate (96.43%) of MRI for bronchial foreign bodies increased obviously in contrast to the rates of X-ray chest radiographs (57.14% and 67.86%) ( < 0.05). Therefore, it was indicated that AFFCM showed higher partition coefficient value, lower segmentation entropy, larger similarity among classes, and better image segmentation effect. Furthermore, AFFCM-based coronal MRI scan had a higher positive rate and diagnosis rate for children's tracheal foreign bodies, and the main signs were emphysema and atelectasis.
为了给临床诊断提供理论支持，研究了优化的模糊 C 均值（FCM）算法与冠状磁共振成像（MRI）扫描相结合在儿童气管异物诊断中的诊断价值。对传统 FCM 算法进行各向异性滤波优化，构建新的 MRI 图像分割算法，即 AFFCM 算法。然后，引入传统 FCM 算法、基于核函数的 FCM 算法（KFCM）和基于空间邻域信息的 FCM 算法（RFCM）与 AFFCM 进行比较。选择 28 例经 MRI 诊断为气管异物的患儿进行 MRI 诊断，使用 AFFCM 进行分割。记录 4 种算法的模糊划分后类划分系数、分割熵以及类间相关度，收集气管异物的位置和分布以及异物类型。此外，还记录了气管异物患儿的 MRI 扫描和胸部 X 线检查的阳性率、诊断率和间接征象。AFFCM 模糊划分后的类划分系数和类间相关度明显大于 FCM、KFCM 和 RFCM（<0.05），而 AFFCM 的分割熵则不如 FCM、KFCM 和 RFCM 的熵尖锐（<0.05）。28 例患儿中，气管异物 5 例（17.86%），左支气管异物 10 例（35.71%），右支气管异物 13 例（46.43%）。异物类型中，瓜子 10 例（35.71%），花生 6 例（21.43%），豆子 5 例（17.86%）。与 X 线胸片相比，MRI 对支气管异物的阳性率（89.29%）和诊断率（96.43%）明显提高（<0.05）。表明 AFFCM 具有较高的类划分系数值、较低的分割熵、较大的类间相似度和较好的图像分割效果。此外，基于 AFFCM 的冠状 MRI 扫描对儿童气管异物的阳性率和诊断率较高，主要征象为肺气肿和肺不张。
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