Abstract
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 (P < 0.05), while the segmentation entropy of AFFCM was less sharp than the entropies of FCM, KFCM, and RFCM (P < 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%) (P < 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.
Highlights
Tracheal foreign bodies in children refer to foreign bodies entering the airway, causing airway blockage
SPSS 19.0 statistical software was used for test data processing, the measurement data were expressed as the mean ± standard deviation (x ± s), the count data were represented by percentage (%), and the AFFCM, fuzzy C-means (FCM), KFCM, and rough FCM (RFCM) were compared by one-way analysis of variance (ANOVA)
The traditional FCM algorithm was first optimized based on anisotropic filtering (AFFCM), which was compared with FCM, KFCM, and RFCM. e result indicated that the partition coefficient and the correlation degree between classes after fuzziness of AFFCM were hugely greater than those of FCM, KFCM, and RFCM, but the segmentation entropy of AFFCM was obviously smaller than those of FCM, KFCM, and RFCM (P < 0.05). is was similar to the research findings of Tamiru et al (2012) [16], which showed that, compared with the traditional algorithm, the proposed algorithm AFFCM had higher segmentation coefficient value, lower segmentation entropy, higher similarity between classes, and better image segmentation effect [17]
Summary
Tracheal foreign bodies in children refer to foreign bodies entering the airway, causing airway blockage In mild cases, it can result in lung damage, and, in severe cases, suffocation death is more common in children below 5 years of age [1]. E most common manifestations of foreign bodies in the trachea of children are severe coughing, suffocation, nausea, excessive phlegm, and difficulty breathing [2]. MRI examination is an auxiliary examination method that is extensively applied in clinical practice It is different from X-ray film, CT, and other methods, which can accurately locate the lesion and make qualitative diagnosis of the lesion. It is mainly suitable for soft tissues, bones and joints, nervous system, and chest and abdomen [6, 7]
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