A review of Methods for Bias Correction in Medical Images

Main Article Content

Shuang Song Yuanjie Zheng Yunlong He

Abstract

Abstract
Bias field in medical images is an undesirable artifact primarily arises from the improper image acquisition process or the specific properties of the imaged object. This artifact can be characterized by a smooth variation of intensities across the image and significantly degrade many medical image analysis techniques. Studies on bias correction have been investigated extensively over these years. In this paper, we proposed to category and analysis existing bias
correction methods, provide a complete review article that enables comparative studies on bias correction in medical images.

Article Details

How to Cite
SONG, Shuang; ZHENG, Yuanjie; HE, Yunlong. A review of Methods for Bias Correction in Medical Images. Biomedical Engineering Review, [S.l.], v. 1, n. 1, sep. 2017. ISSN 2375-9151. Available at: <http://journals.ke-i.org/index.php/bme/article/view/1550>. Date accessed: 15 dec. 2017. doi: https://doi.org/10.18103/bme.v3i1.1550.
Section
Articles

References

References

Adhikari, S. K., Sing, J. K., & Basu, D. K. (2016). Bias field esti- mation and segmentation of mri images using a spatial fuzzy c- means algorithm. In Control, instrumentation, energy & commu- nication (ciec), 2016 2nd international conference on (pp. 158– 162).



Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Mo- riarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE transactions on medical imaging, 21(3), 193–199.
Aldrich, J., et al. (1997). Ra fisher and the making of maximum likelihood 1912-1922. Statistical Science, 12(3), 162–176.
Alecci, M., Collins, C. M., Smith, M. B., & Jezzard, P. (2001). Ra- dio frequency magnetic field mapping of a 3 tesla birdcage coil: experimental and theoretical dependence on sample properties. Magnetic resonance in medicine, 46(2), 379–385.
Aparajeeta, J., Nanda, P. K., & Das, N. (2016). Modified possi- bilistic fuzzy c-means algorithms for segmentation of magnetic resonance image. Applied Soft Computing, 41, 104–119.
Ardizzone, E., Pirrone, R., & Gambino, O. (2005). Frequency determined homomorphic unsharp masking algorithm on knee mr images. In International conference on image analysis and processing (pp. 922–929).
Ardizzone, E., Pirrone, R., Gambino, O., & Vitabile, S. (2014).
Illumination correction on biomedical images. Computing and Informatics, 33(1), 175–196.
Arnold, J. B., Liow, J.-S., Schaper, K. A., Stern, J. J., Sled, J. G., Shattuck, D. W., . . . others (2001). Qualitative and quantitative evaluation of six algorithms for correcting intensity nonunifor- mity effects. NeuroImage, 13(5), 931–943.
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neu- roimage, 26(3), 839–851.
Axel, L., Costantini, J., & Listerud, J. (1987). Intensity correction in surface-coil mr imaging. American Journal of Roentgenology, 148(2), 418–420.
Banerjee, A., & Maji, P. (2015). Rough sets and stomped normal distribution for simultaneous segmentation and bias field correc- tion in brain mr images. IEEE Transactions on Image Process- ing, 24(12), 5764–5776.
Bansal, R., Staib, L. H., & Peterson, B. S. (2004). Correcting nonuniformities in mri intensities using entropy minimization based on an elastic model. In International conference on med- ical image computing and computer-assisted intervention (pp. 78–86).
Belaroussi, B., Milles, J., Carme, S., Zhu, Y. M., & Benoit-Cattin,
H. (2006). Intensity non-uniformity correction in mri: existing methods and their validation. Medical Image Analysis, 10(2), 234–246.
Boroomand, A., Shafiee, M. J., Khalvati, F., Haider, M. A., & Wong, A. (2015). Noise-compensated, bias-corrected diffusion weighted endorectal magnetic resonance imaging via a stochas- tically fully-connected joint conditional random field model. arXiv preprint arXiv:1512.04636.
Boyes, R. G., Gunter, J. L., Frost, C., Janke, A. L., Yeatman, T., Hill, D. L., . . . others (2008). Intensity non-uniformity cor- rection using n3 on 3-t scanners with multichannel phased array coils. Neuroimage, 39(4), 1752–1762.
Brechbühler, C., Gerig, G., & Székely, G. (1996). Compensation of spatial inhomogeneity in mri based on a parametric bias esti- mate. In Visualization in biomedical computing (pp. 141–146).
Brey, W. W., & Narayana, P. A. (1988). Correction for inten- sity falloff in surface coil magnetic resonance imaging. Medical Physics, 15(2), 241–245.
Brinkmann, B. H., Manduca, A., & Robb, R. A. (1998). Optimized

homomorphic unsharp masking for mr grayscale inhomogeneity correction. IEEE transactions on medical imaging, 17(2), 161– 171.
Chang, H., Huang, W., Wu, C., Huang, S., Guan, C., Sekar, S., . . .
Duan, Y. (2016). A new variational method for bias correction and its applications to rodent brain extraction. IEEE Transac- tions on Medical Imaging.
Chen, Y., Zhang, H., Zheng, Y., Jeon, B., & Wu, Q. J. (2016). An improved anisotropic hierarchical fuzzy c-means method based on multivariate student t-distribution for brain mri segmentation. Pattern Recognition, 60, 778–792.
Chiou, J.-Y., Ahn, C. B., Muftuler, L. T., & Nalcioglu, O. (2003). A simple simultaneous geometric and intensity correction method for echo-planar imaging by epi-based phase modulation. IEEE transactions on medical imaging, 22(2), 200–205.
Cohen, M. S., DuBois, R. M., & Zeineh, M. M. (2000). Rapid and effective correction of rf inhomogeneity for high field magnetic resonance imaging. Human brain mapping, 10(4), 204–211.
Collewet, G., Davenel, A., Toussaint, C., & Akoka, S. (2002). Cor- rection of intensity nonuniformity in spin-echo t 1-weighted im- ages. Magnetic resonance imaging, 20(4), 365–373.
Condon, B., Patterson, J., Wyper, D., Jenkins, A., & Hadley, D. (1987). Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction. The British journal of radiology, 60(709), 83–87.
Davenel, A., Marchal, P., Riaublanc, A., Gandemer, G., Belton, P., Hills, B., & Webb, G. (1999). Magnetic resonance map- ping of solid fat content of adipose tissues in meat. SPECIAL PUBLICATION-ROYAL SOCIETY OF CHEMISTRY, 231, 272– 279.
Dawant, B. M., Zijdenbos, A. P., & Margolin, R. A. (1993). Correc- tion of intensity variations in mr images for computer-aided tis- sue classification. IEEE transactions on medical imaging, 12(4), 770–781.
Fan, A., Wells, W. M., Fisher, J. W., Cetin, M., Haker, S., Mulkern, R., . . . Willsky, A. S. (2003). A unified variational approach to denoising and bias correction in mr. In Biennial international conference on information processing in medical imaging (pp. 148–159).
Gispert, J. D., Reig, S., Pascau, J., Lazaro, M., Vaquero, J. J.,
& Desco, M. (2003). Inhomogeneity correction of magnetic resonance images by minimization of intensity overlapping. In Image processing, 2003. icip 2003. proceedings. 2003 interna- tional conference on (Vol. 2, pp. II–847).
Gispert, J. D., Reig, S., Pascau, J., Vaquero, J. J., García-Barreno, P., & Desco, M. (2004). Method for bias field correction of brain t1-weighted magnetic resonance images minimizing segmenta- tion error. Human brain mapping, 22(2), 133–144.
Gubern-Mérida, A., Martí, R., Melendez, J., Hauth, J. L., Mann,
R. M., Karssemeijer, N., & Platel, B. (2015). Automated lo- calization of breast cancer in dce-mri. Medical image analysis, 20(1), 265–274.
Guillemaud, R., & Brady, M. (1997). Estimating the bias field of mr images. IEEE Transactions on Medical imaging, 16(3), 238–251.
Han, C., Hatsukami, T. S., & Yuan, C. (2001). A multi-scale method for automatic correction of intensity non-uniformity in mr images. Journal of Magnetic Resonance Imaging, 13(3),



428–436.
Haselgrove, J., & Prammer, M. (1986). An algorithm for compen-

posteriori probability. Medical Physics, 32(7), 2337–2345. Liang, Z.-P. ., & Lauterbur, P. C. (2000). Principles of magnetic

sation of surface-coil images for sensitivity of the surface coil.
Magnetic Resonance Imaging, 4(6), 469–472.

resonance imaging: a signal processing perspective.
Institute of Electrical and Electronics Engineers Press.

a˛ˇrThea˛s´

Hou, Z. (2006). A review on mr image intensity inhomogeneity correction. International Journal of Biomedical Imaging, 2006. James, C. B. (1981). Pattern recognition with fuzzy objective func-
tion algorithms. Kluwer Academic Publishers.
Johnston, B., Atkins, M. S., Mackiewich, B., & Anderson, M. (1996). Segmentation of multiple sclerosis lesions in inten- sity corrected multispectral mri. IEEE Transactions on Medical Imaging, 15(2), 154–169.
Keiper, M. D., Grossman, R. I., Hirsch, J. A., Bolinger, L., Ott,
I. L., Mannon, L. J., . . . Kolson, D. L. (1998). Mr identification of white matter abnormalities in multiple sclerosis: a compari- son between 1.5 t and 4 t. American journal of neuroradiology, 19(8), 1489–1493.
Kim, S.-G., Ng, S.-K., McLachlan, G., & Wang, D. (2003). Seg- mentation of brain mr images with bias field correction. In Work- shop on digital image computing (pp. 3–8).
Lai, S.-H., & Fang, M. (1999). A new variational shape-from- orientation approach to correcting intensity inhomogeneities in magnetic resonance images. Medical Image Analysis, 3(4), 409– 424.
Lai, S.-H., & Fang, M. (2003). A dual image approach for bias field correction in magnetic resonance imaging. Magnetic resonance imaging, 21(2), 121–125.
Lee, J.-H., Marzelli, M., Jolesz, F. A., & Yoo, S.-S. (2009). Auto- mated classification of fmri data employing trial-based imagery tasks. Medical image analysis, 13(3), 392–404.
Lee, S. K., & Vannier, M. W. (1996). Post-acquisition correction of mr inhomogeneities. Magnetic Resonance in Medicine, 36(2), 275–286.
Lefkovits, L., Lefkovits, S., & Vaida, M.-F. (2015). An atlas based performance evaluation of inhomogeneity correcting ef- fects. MACRo 2015, 1(1), 79–90.
Lewis, E. B., & Fox, N. C. (2004). Correction of differential in- tensity inhomogeneity in longitudinal mr images. Neuroimage, 23(1), 75–83.
Li, C., Gore, J. C., & Davatzikos, C. (2014). Multiplicative intrinsic component optimization (mico) for mri bias field estimation and tissue segmentation. Magnetic resonance imaging, 32(7), 913– 923.
Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D. N., & Gore,
J. C. (2011). A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri. IEEE Transactions on Image Processing, 20(7), 2007–2016.
Li, C., Li, F., Kao, C.-Y., & Xu, C. (2009). Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach. In 2009 ieee 12th international conference on computer vision (pp. 702–708).
Li, C., Xu, C., Anderson, A. W., & Gore, J. C. (2009). Mri tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework. In International conference on information processing in medi- cal imaging (pp. 288–299).
Li, X., Li, L., Lu, H., & Liang, Z. (2005). Partial volume segmen- tation of brain magnetic resonance images based on maximum a

Likar, B., Maintz, J. A., Viergever, M. A., Pernus, F., et al. (2000).
Retrospective shading correction based on entropy minimiza- tion. Journal of Microscopy, 197(3), 285–295.
Likar, B., Viergever, M. A., & Pernus, F. (2001). Retrospective correction of mr intensity inhomogeneity by information mini- mization. IEEE transactions on medical imaging, 20(12), 1398– 1410.
Lin, F.-H., Chen, Y.-J., Belliveau, J. W., & Wald, L. L. (2003).
A wavelet-based approximation of surface coil sensitivity pro- files for correction of image intensity inhomogeneity and paral- lel imaging reconstruction. Human brain mapping, 19(2), 96– 111.
Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J. C., Quiles, A., . . . Rovira, À. (2012). Segmentation of multiple sclerosis lesions in brain mri: a review of automated approaches. Information Sciences, 186(1), 164–185.
Lui, D., Modhafar, A., Glaister, J., Wong, A., & Haider, M. A. (2014). Monte carlo bias field correction in endorectal diffusion imaging. IEEE Transactions on Biomedical Engineering, 61(2), 368–380.
Luo, J., Zhu, Y., Clarysse, P., & Magnin, I. (2005). Correction of bias field in mr images using singularity function analysis. IEEE transactions on medical imaging, 24(8), 1067–1085.
Mangin, J.-F. (2000). Entropy minimization for automatic cor- rection of intensity nonuniformity. In Mathematical methods in biomedical image analysis, 2000. proceedings. ieee workshop on (pp. 162–169).
McVeigh, E. R., Bronskill, M., & Henkelman, R. (1986). Phase and sensitivity of receiver coils in magnetic resonance imaging. Medical physics, 13(6), 806–814.
Meyer, C. R., Bland, P. H., & Pipe, J. (1995). Retrospective cor- rection of intensity inhomogeneities in mri. IEEE Transactions on Medical Imaging, 14(1), 36–41.
Miao, Y., Dai, J., Li, Y., He, W., Shi, W., He, F., . . . Zhang, H.
(2016). An mr image segmentation algorithm based on bias field correction. In Natural computation, fuzzy systems and knowl- edge discovery (icnc-fskd), 2016 12th international conference on (pp. 1858–1862).
Mihara, H., Iriguchi, N., & Ueno, S. (1998). A method of rf in- homogeneity correction in mr imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 7(2), 115–120.
Milles, J., Zhu, Y. M., Gimenez, G., Guttmann, C. R., & Magnin,
I. E. (2007). Mri intensity nonuniformity correction using simul- taneously spatial and gray-level histogram information. Com- puterized Medical Imaging and Graphics, 31(2), 81–90.
Moyher, S. E., Vigneron, D. B., & Nelson, S. J. (1995). Surface coil mr imaging of the human brain with an analytic reception profile correction. Journal of Magnetic Resonance Imaging, 5(2), 139– 144.
Murakami, J. W., Hayes, C. E., & Weinberger, E. (1996). Inten- sity correction of phased-array surface coil images. Magnetic Resonance in Medicine, 35(4), 585–590.
Narayana, P., Brey, W., Kulkarni, M., & Sievenpiper, C. (1988).
Compensation for surface coil sensitivity variation in magnetic



resonance imaging. Magnetic resonance imaging, 6(3), 271– 274.
Narayana, P. A., & Borthakur, A. (1995). Effect of radio frequency inhomogeneity correction on the reproducibility of intra-cranial volumes using mr image data. Magnetic Resonance in Medicine, 33(3), 396–400.
Nascimento, A. D., Frery, A. C., & Cintra, R. J. (2014). Bias cor- rection and modified profile likelihood under the wishart com- plex distribution. IEEE Transactions on Geoscience and Remote Sensing, 52(8), 4932–4941.
Olshausen, B. A., et al. (1996). Emergence of simple-cell recep- tive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609.
Pham, D. L. (2001). Spatial models for fuzzy clustering. Computer vision and image understanding, 84(2), 285–297.
Pham, D. L., & Prince, J. L. (1999). Adaptive fuzzy segmentation of magnetic resonance images. IEEE transactions on medical imaging, 18(9), 737–752.
Pop, P., Vaida, M.-F., et al. (2015). Bias field inhomogeneity mea- surements. In E-health and bioengineering conference (ehb), 2015 (pp. 1–4).
Prima, S., Ayache, N., Barrick, T., & Roberts, N. (2001). Maximum likelihood estimation of the bias field in mr brain images: Inves- tigating different modelings of the imaging process. In Interna- tional conference on medical image computing and computer- assisted intervention (pp. 811–819).
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., Boesiger, P., et al. (1999). Sense: sensitivity encoding for fast mri. Magnetic resonance in medicine, 42(5), 952–962.
Santha Kumari, T., Suresh, B., Yashwanth, P., & Rao, R. R. (2015).
Modified histogram based contrast enhancement using unsharp masking filter for medical images. IJRCCT, 4(2), 137–140.
Seshamani, S., Cheng, X., Fogtmann, M., Thomason, M. E., & Studholme, C. (2014). A method for handling intensity inho- mogenieties in fmri sequences of moving anatomy of the early developing brain. Medical image analysis, 18(2), 285–300.
Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg,
D. A., & Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 13(5), 856–876.
Shimazaki, H., & Shinomoto, S. (2007). A method for selecting the bin size of a time histogram. Neural computation, 19(6), 1503–1527.
Sijbers, J., den Dekker, A. J., Scheunders, P., & Van Dyck, D. (1998). Maximum-likelihood estimation of rician distribution parameters. IEEE Trans. Med. Imaging, 17(3), 357–361.
Simmons, A., Tofts, P. S., Barker, G. J., & Arridge, S. R. (1994).
Sources of intensity nonuniformity in spin echo images at 1.5 t.
Magnetic Resonance in Medicine, 32(1), 121–128.
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparamet- ric method for automatic correction of intensity nonuniformity in mri data. IEEE transactions on medical imaging, 17(1), 87– 97.
Sreenivasan, K. R., Havlicek, M., & Deshpande, G. (2015). Non- parametric hemodynamic deconvolution of fmri using homo- morphic filtering. IEEE transactions on medical imaging, 34(5), 1155–1163.
Styner, M., Brechbuhler, C., Szckely, G., & Gerig, G. (2000).

Parametric estimate of intensity inhomogeneities applied to mri.
IEEE transactions on medical imaging, 19(3), 153–165. Thulborn, K. R., Boada, F. E., Shen, G. X., Christensen, J. D., &
Reese, T. G. (1998). Correction of b1 inhomogeneities using
echo-planar imaging of water. Magnetic resonance in medicine, 39(3), 369–375.
Tincher, M., Meyer, C., Gupta, R., & Williams, D. (1993). Poly- nomial modeling and reduction of rf body coil spatial inhomo- geneity in mri. IEEE transactions on medical imaging, 12(2), 361–365.
Tofts, P. S., Barker, G. J., Simmons, A., MacManus, D. G., Thorpe, J., Gass, A., & Miller, D. H. (1994). Correction of nonunifor- mity in images of the spine and optic nerve from fixed receive- only surface coils at 1.5 t. Journal of computer assisted tomog- raphy, 18(6), 997–1003.
Tomaževicˇ, D., Likar, B., & Pernuš, F. (2002). Comparative eval- uation of retrospective shading correction methods. Journal of Microscopy, 208(3), 212–223.
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4itk: improved n3 bias correction. IEEE transactions on medical imaging, 29(6), 1310–1320.
Valverde, S., Oliver, A., Roura, E., González-Villà, S., Pareto, D., Vilanova, J. C., . . . Lladó, X. (2017). Automated tissue segmen- tation of mr brain images in the presence of white matter lesions. Medical image analysis, 35, 446–457.
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999). Automated model-based bias field correction of mr images of the brain. IEEE transactions on medical imaging, 18(10), 885–896.
Velthuizen, R. P., Heine, J. J., Cantor, A. B., Lin, H., Fletcher, L. M.,
& Clarke, L. P. (1998). Review and evaluation of mri nonunifor- mity corrections for brain tumor response measurements. Medi- cal physics, 25(9), 1655–1666.
Vemuri, P., Kholmovski, E. G., Parker, D. L., & Chapman, B. E. (2005). Coil sensitivity estimation for optimal snr reconstruc- tion and intensity inhomogeneity correction in phased array mr imaging. In Biennial international conference on information processing in medical imaging (pp. 603–614).
Viola, P., & Wells III, W. M. (1997). Alignment by maximization of mutual information. International journal of computer vision, 24(2), 137–154.
Vokurka, E. A., Thacker, N. A., & Jackson, A. (1999). A fast model independent method for automatic correction of inten- sity nonuniformity in mri data. Journal of Magnetic Resonance Imaging, 10(4), 550–562.
Vokurka, E. A., Watson, N. A., Watson, Y., Thacker, N. A., & Jack- son, A. (2001). Improved high resolution mr imaging for sur- face coils using automated intensity non-uniformity correction: Feasibility study in the orbit. Journal of Magnetic Resonance Imaging, 14(5), 540–546.
Vovk, U., Pernuš, F., & Likar, B. (2004). Mri intensity inhomogene- ity correction by combining intensity and spatial information. Physics in Medicine and Biology, 49(17), 4119.
Vovk, U., Pernuš, F., & Likar, B. (2006). Intensity inhomogeneity correction of multispectral mr images. Neuroimage, 32(1), 54– 61.
Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for



correction of intensity inhomogeneity in mri. IEEE transactions on medical imaging, 26(3), 405–421.
Weese, J., & Lorenz, C. (2016). Four challenges in medical image analysis from an industrial perspective. Medical Image Analysis, 33, 44–49.
Wells, W. M., Grimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996). Adaptive segmentation of mri data. IEEE transactions on medical imaging, 15(4), 429–442.
Wicks, D. A., Barker, G. J., & Tofts, P. S. (1993). Correction of in- tensity nonuniformity in mr images of any orientation. Magnetic Resonance Imaging, 11(2), 183–196.
Xie, M., Gao, J., Zhu, C., & Zhou, Y. (2015). A modified method for mrf segmentation and bias correction of mr image with inten- sity inhomogeneity. Medical & biological engineering & com- puting, 53(1), 23–35.
Xu, L., Wan, J. W., & Bian, T. (2013). A continuous method for re- ducing interpolation artifacts in mutual information-based rigid image registration. IEEE Transactions on Image Processing, 22(8), 2995–3007.
Yang, D., Gach, H., Li, H., & Mutic, S. (2016). Tu-h-206-04: An
effective homomorphic unsharp mask filtering method to correct intensity inhomogeneity in daily treatment mr images. Medical Physics, 43(6), 3774–3774.
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain mr images through a hidden markov random field model and

the expectation-maximization algorithm. IEEE transactions on medical imaging, 20(1), 45–57.
Zheng, W., Chee, M. W., & Zagorodnov, V. (2009). Improvement of brain segmentation accuracy by optimizing non-uniformity correction using n3. Neuroimage, 48(1), 73–83.
Zheng, Y., Grossman, M., Awate, S., & Gee, J. (2009). Automatic correction of intensity nonuniformity from sparseness of gradi- ent distribution in medical images. In Medical image comput- ing and computer-assisted intervention: Miccai... international conference on medical image computing and computer-assisted intervention (Vol. 12, p. 852).
Zheng, Y., Yu, J., Kang, S. B., Lin, S., & Kambhamettu, C. (2008).
Single-image vignetting correction using radial gradient sym- metry. In Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on (pp. 1–8).
Zhou, L., Zhu, Y., Bergot, C., Laval-Jeantet, A.-M., Bousson, V., Laredo, J.-D., & Laval-Jeantet, M. (2001). A method of radio- frequency inhomogeneity correction for brain tissue segmen- tation in mri. Computerized Medical Imaging and Graphics, 25(5), 379–389.
Zhuge, Y., Udupa, J. K., Liu, J., Saha, P. K., & Iwanage, T. (2002).
Scale-based method for correcting background intensity varia- tion in acquired images. In Medical imaging 2002 (pp. 1103– 1111).

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.