MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule
| Autor: | Yanli Zheng, Lingang Wang, Mei-Hua Zhang, Bo Feng, Han-Lin Zhu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Lung Neoplasms Support Vector Machine Article Subject 030218 nuclear medicine & medical imaging Young Adult 03 medical and health sciences Mri image 0302 clinical medicine Support vector machine algorithm Medical technology Image Processing Computer-Assisted medicine Humans Operation time Effective diffusion coefficient Radiology Nuclear Medicine and imaging Segmentation R855-855.5 Aged Mathematics Solitary pulmonary nodule business.industry Solitary Pulmonary Nodule Middle Aged Prognosis medicine.disease Support vector machine Diffusion Magnetic Resonance Imaging 030220 oncology & carcinogenesis Female Nuclear medicine business Algorithms Research Article Follow-Up Studies |
| Zdroj: | Contrast Media & Molecular Imaging Contrast Media & Molecular Imaging, Vol 2021 (2021) |
| ISSN: | 1555-4317 1555-4309 |
| DOI: | 10.1155/2021/9668836 |
| Popis: | This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences ( P > 0.05 ) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group ( P < 0.01 ). The ADC value with different b values in the benign group was significantly lower than that of the malignant group ( P < 0.01 ). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients. |
| Databáze: | OpenAIRE |
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