Abstract
The study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM). First, a method of combining the maximum and minimum distances was proposed to improve the FCM algorithm. Then, the optimized FCM was applied to the diffusion tensor imaging (DTI) diagnosis. The patients were rolled into the benign tumor group and the malignant tumor group, and relevant parameters were compared. Finally, the postoperative total resection rate and disability rate of the DTI experimental group and the traditional control (Ctrl) group were evaluated. It was found that the segmentation accuracy of the optimized FCM algorithm was higher than traditional one and the obtained corpus callosum edge contour was clearer. In 63 patients with intracranial space, there were obvious differences in pairwise comparison of meningioma and glioma, metastatic tumor’s apparent diffusion coefficient (ADC) value, relative apparent diffusion coefficient (r ADC) value, and relative anisotropy fraction (r FA) P < 0.05 . In terms of the ADC, r ADC, and r FA values of tumor parenchymal area, those of benign tumors were larger than malignant tumors P < 0.05 . The ADC value (8.21 ± 1.87) and r FA value (1.36 ± 0.41) of the contralateral normal white matter area of malignant tumor were greater than the ADC value (7.23 ± 2.31) and r FA value (0.61 ± 0.24) of the peritumor white matter area, with statistically significant differences P < 0.05 . The total cut rates of the experimental group and the Ctrl were 87.5% and 54.84%, and the disability rates were 6.25% and 34.38%. In conclusion, the optimized FCM has high accuracy. The ADC, r ADC, and r FA values of DTI are important in the diagnosis of intracranial tumors. Besides, DTI can improve the survival rate in guiding intracranial tumor resection.
Highlights
Academic Editor: Gustavo Ramirez e study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM)
In terms of the apparent diffusion coefficient (ADC), relative apparent diffusion coefficient (r ADC), and relative anisotropy fraction (r FA) values of tumor parenchymal area, those of benign tumors were larger than malignant tumors (P < 0.05). e ADC value (8.21 ± 1.87) and r FA value (1.36 ± 0.41) of the contralateral normal white matter area of malignant tumor were greater than the ADC value (7.23 ± 2.31) and r FA value (0.61 ± 0.24) of the peritumor white matter area, with statistically significant differences (P < 0.05). e total cut rates of the experimental group and the Ctrl were 87.5% and 54.84%, and the disability rates were 6.25% and 34.38%
Ali et al (2015) [10] used morphological pyramid to combine multi-resolution image and original image, and segmented human brain image by the FCM algorithm. en, the accuracy of the final result was improved. e traditional FCM algorithm has randomness in the selection method of the initial center point. e initial center point can be determined by the combination of the maximum and minimum distances. e FCM algorithm is optimized to improve the accuracy of the tissue region segmentation in the DTI data, which provides excellent analytical methods [11, 12]
Summary
Received 8 June 2021; Revised 30 June 2021; Accepted 28 July 2021; Published 2 August 2021. E study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM). DTI is a new imaging technology that uses the principle of water molecular diffusion to detect the microstructure of living tissues on the basis of diffuse weighted imaging (DWI) It features being noninvasive and no need for contrast agents [8, 9]. E FCM algorithm is optimized to improve the accuracy of the tissue region segmentation in the DTI data, which provides excellent analytical methods [11, 12]. The traditional FCM clustering algorithm was optimized by combining the maximum and minimum distances, so as to determine the initial center point. En, the optimized FCM clustering algorithm was applied to DTI images of benign and malignant intracranial tumors The traditional FCM clustering algorithm was optimized by combining the maximum and minimum distances, so as to determine the initial center point. en, the optimized FCM clustering algorithm was applied to DTI images of benign and malignant intracranial tumors
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