Abstract
Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.
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References
Collins, D., Dai, W., Peters, T., Evans, A.: Automatic 3D model-based neuroanatomical segmentation. Human Brain Mapping 3, 190–208 (1995)
Chalana, V., Kim, Y.: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Transactions on Medical Imaging 16(5), 642–652 (1997)
Beauchemin, M., Thomson, K.P.B.: The evaluation of segmentation results and the overlapping area matrix. International Journal of Remote Sensing 18(18), 3895–3899 (1997)
Bello, F., Colchester, A.C.F.: Measuring global and local spatial correspondence using information theory. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 964–973. Springer, Heidelberg (1998)
Gerig, G., Jomier, M., Chakos, M.: Valmet: A new validation tool for assessing and improving 3D object segmentation. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 516–528. Springer, Heidelberg (2001)
Crum, W.R., Griffin, L.D., Hill, D.L.G., Hawkes, D.J.: Zen and the Art of Medical Image Registration: Correspondence, Homology and Quality. NeuroImage 20, 1425–1437 (2003)
Bloch, I.: Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing 23(2), 89–110 (2005)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Bhatia, K.K., Hajnal, J.V., Puri, B.K., Edwards, A.D., Rueckert, D.: Consistent groupwise non-rigid registration for atlas construction. In: IEEE Symposium on Biomedical Imaging (ISBI), Arlington, pp. 908–911 (2004)
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Crum, W.R., Camara, O., Rueckert, D., Bhatia, K.K., Jenkinson, M., Hill, D.L.G. (2005). Generalised Overlap Measures for Assessment of Pairwise and Groupwise Image Registration and Segmentation. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_13
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DOI: https://doi.org/10.1007/11566465_13
Publisher Name: Springer, Berlin, Heidelberg
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