Computational and Mathematical Methods in Medicine / 2013 / Article / Tab 1 / Research Article
Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior Table 1 Similarity measure with the Jaccard and Dice indices, Hausdorff distance, and the Maximum cardinality similarity metric among the regions segmented by the Graph Cut method, traditional ACM, interactive Tseng method, our proposed method, and the regions outlined by experts of the CT dataset.
Test Graph Cut versus experts ACM versus experts Tseng versus experts Our method versus experts Image (J) (D) (H) (MCSM) (J) (D) (H) (MCSM) (J) (D) (H) (MCSM) (J) (D) (H) (MCSM) 1 0.551 0.711 4.000 0.343 0.698 0.822 10.049 0.681 0.636 0.777 10.198 0.781 0.836 0.911 1.036 0.759 5 0.698 0.822 5.385 0.497 0.636 0.777 5.099 0.396 0.666 0.800 10.000 0.644 0.800 0.888 3.989 0.833 10 0.607 0.755 3.605 0.374 0.607 0.755 4.472 0.452 0.578 0.733 5.000 0.684 0.764 0.866 1.000 0.813 15 0.475 0.644 10.000 0.512 0.836 0.911 6.580 0.695 0.800 0.888 7.211 0.422 0.875 0.933 2.719 0.793 20 0.428 0.600 2.828 0.449 0.875 0.933 7.214 0.660 0.836 0.911 10.000 0.552 0.914 0.955 3.105 0.764 25 0.800 0.888 4.900 0.475 0.914 0.955 5.000 0.723 0.698 0.822 7.000 0.511 0.764 0.866 1.381 0.729 30 0.730 0.844 2.236 0.386 0.607 0.755 2.828 0.621 0.636 0.777 5.385 0.748 0.730 0.844 2.828 0.790 35 0.636 0.777 5.000 0.550 0.551 0.711 1.000 0.720 0.875 0.933 2.828 0.781 0.914 0.955 1.082 0.920 40 0.607 0.755 4.500 0.493 0.500 0.666 4.123 0.740 0.800 0.888 2.000 0.755 0.875 0.933 6.000 0.797 45 0.525 0.688 10.414 0.500 0.525 0.688 5.000 0.617 0.607 0.755 3.000 0.835 0.636 0.777 1.082 0.863 50 0.451 0.622 9.798 0.469 0.698 0.822 18.384 0.601 0.730 0.844 1.414 0.519 0.698 0.822 2.828 0.749 55 0.428 0.600 6.082 0.434 0.764 0.866 12.529 0.419 0.800 0.888 1.000 0.430 0.836 0.911 5.099 0.645 60 0.764 0.866 9.848 0.439 0.666 0.800 2.236 0.692 0.730 0.844 1.000 0.509 0.764 0.866 1.000 0.778 65 0.875 0.933 11.313 0.394 0.914 0.955 8.000 0.616 0.875 0.933 8.000 0.615 0.800 0.888 8.000 0.635 70 0.451 0.622 16.278 0.467 0.525 0.688 1.000 0.688 0.607 0.755 3.000 0.505 0.578 0.733 5.236 0.863 75 0.500 0.666 19.798 0.484 0.551 0.711 2.236 0.561 0.525 0.688 2.828 0.683 0.607 0.755 2.828 0.655 80 0.551 0.711 14.866 0.398 0.607 0.755 5.000 0.504 0.578 0.733 2.000 0.879 0.666 0.800 5.000 0.843 85 0.578 0.733 12.236 0.394 0.666 0.800 3.162 0.542 0.698 0.822 3.083 0.827 0.730 0.844 4.885 0.737 90 0.698 0.822 6.403 0.468 0.764 0.866 4.123 0.511 0.800 0.888 9.433 0.534 0.764 0.866 6.336 0.832 95 0.764 0.866 13.605 0.502 0.836 0.911 11.401 0.567 0.836 0.911 5.099 0.687 0.800 0.888 8.000 0.904 100 0.525 0.688 14.123 0.467 0.578 0.733 1.000 0.576 0.551 0.711 1.000 0.579 0.636 0.777 2.236 0.878 105 0.406 0.577 1.414 0.523 0.475 0.644 13.601 0.604 0.607 0.755 5.385 0.632 0.607 0.755 1.414 0.838 110 0.384 0.555 6.0 0.602 0.428 0.600 5.000 0.691 0.500 0.666 7.000 0.575 0.551 0.711 8.000 0.776 115 0.836 0.911 8.944 0.586 0.800 0.888 13.038 0.461 0.836 0.911 4.472 0.523 0.875 0.933 4.242 0.869 120 0.764 0.866 9.848 0.514 0.836 0.911 15.231 0.695 0.764 0.866 6.000 0.663 0.836 0.911 6.000 0.817 125 0.666 0.800 13.601 0.611 0.875 0.933 14.142 0.609 0.914 0.955 6.708 0.718 0.956 0.977 3.000 0.948 130 0.578 0.733 10.770 0.598 0.607 0.755 12.649 0.677 0.875 0.933 7.280 0.618 0.914 0.955 4.123 0.843 135 0.698 0.822 11.401 0.487 0.698 0.822 17.720 0.741 0.956 0.977 5.000 0.681 0.956 0.977 2.236 0.705 140 0.636 0.777 8.540 0.534 0.764 0.866 11.704 0.588 0.836 0.911 1.000 0.653 0.875 0.933 2.000 0.834 144 0.525 0.688 6.827 0.568 0.578 0.733 7.280 0.619 0.730 0.844 10.000 0.843 0.914 0.955 1.000 0.935 Average 0.607 0.755 8.485 0.546 0.666 0.800 7.182 0.692 0.764 0.866 5.716 0.778 0.875 0.933 5.228 0.856