| Autor: |
Mahmoudi, Ramzi, Benameur, Narjes, Badii, Hmida, Bedoui, Momahed Hedi |
| Předmět: |
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| Zdroj: |
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; Dec 2021, Vol. 9 Issue 6, p678-689, 12p |
| Abstrakt: |
Early detection of chordae tendineae state through medical imaging has a significant impact in reducing the clinical progression of mitral valve disease. This study proposes two methods for tracking these structures. The first method is based on Frangi filter to detect tubular objects while the second one is mainly based on ISODATA clustering approaches. Twenty-eight patients who had previously undergone CT study for mitral valve evaluation were retrospectively enrolled in the study. Pearson correlation coefficients and a linear regression analysis were used to evaluate the correlation between clinical indices measurements for mitral valve segmentation derived from manual segmentation and those obtained through two semi-automated methods. The results showed that MV segmentation based on Frangi filter gave an average Dice index of 0.79 ± 1.52 and a HD of 2.67± 1.14 mm while the ISODATA algorithm indicated an average Dice index of 0.65 ± 0.24 and a HD of 3.26± 0.95 mm. The Pearson correlation coefficients showed a strong correlation between clinical measurements (r= 0.921 for P-A diameter and r=0.987 for MA area, p < 0.01) for mitral valve segmentation derived from manual segmentation and those obtained through Frangi filter method. Similarly, we reported a good correlation between ISDATA and manual segmentation in the measurement of P-A diameter and MA area with respectively r = 0.872 and r = 0.859. The Frangi filter algorithm performs better than the ISODATA algorithm. This suggests that MV segmentation using Frangi filter algorithm gives a performance, which is quite similar to the expert human segmentation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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