http://iet.metastore.ingenta.com
1887

Mutual information-based binarisation of multiple images of an object: an application in medical imaging

Mutual information-based binarisation of multiple images of an object: an application in medical imaging

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A new method for image thresholding of two or more images that are acquired in different modalities or acquisition protocols is proposed. The method is based on measures from information theory and has no underlying free parameters nor does it require training or calibration. The method is based on finding an optimal set of global thresholds, one for each image, by maximising the mutual information above the thresholds while minimising the mutual information below the thresholds. Although some assumptions on the nature of images are made, no assumptions are made by the method on the intensity distributions or on the shape of the image histograms. The effectiveness of the method is demonstrated both on synthetic images and medical images from clinical practice. It is then compared against three other thresholding methods

References

    1. 1)
      • 1. Snyder, W.E., Qi, H.: ‘Machine vision’ (Cambridge University Press, Cambridge, UK, 2004).
    2. 2)
      • 2. Sezgin, M., Sankur, B.: ‘Survey over image thresholding techniques and quantitative performance evaluation’, J. Electron. Imaging, 2004, 13, (1), pp. 146168, Society of Photo-Optical Instrumentation Engineers (doi: 10.1117/1.1631315).
    3. 3)
      • 3. Ma, Z., Tavares, J.M., Jorge, R.N., Mascarenhas, T.: ‘A review of algorithms for medical image segmentation and their applications to the female pelvic cavity’, Comput. Methods Biomech. Biomed. Eng., 2009, 13, (2), pp. 235246 (doi: 10.1080/10255840903131878).
    4. 4)
      • 4. Conaire, O., O'Connor, N.E., Cooke, E., Smeaton, A.F.: ‘Detection thresholding using mutual information’. Int. Conf. on Computer Vision Theory and Applications, 2006.
    5. 5)
      • 5. Althouse, M.L., Chang, C.I.: ‘Target detection in multispectral images using the spectral co-occurrence matrix and entropy thresholding’, Opt. Eng., 1995, 34, (07), pp. 21352148 (doi: 10.1117/12.206579).
    6. 6)
      • 6. Abutableb, A.S.: ‘Automatic thresholding of gray-level pictures using two-dimensional entropy’, Comput. Vis. Graph. Image Process., 1989, 47, (1), pp. 2232 (doi: 10.1016/0734-189X(89)90051-0).
    7. 7)
      • 7. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Prentice-Hall, 2002, 2nd edn.).
    8. 8)
      • 8. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, SMC-9, (1), pp. 6266.
    9. 9)
      • 9. Arifin, A.Z., Asano, A.: ‘Image segmentation by histogram thresholding using hierarchical cluster analysis’, Pattern Recognit. Lett., 2006, 27, (13), pp. 15151521 (doi: 10.1016/j.patrec.2006.02.022).
    10. 10)
      • 10. Cheng, H.D., Chen, Y.H., Jiang, X.: ‘Thresholding using two-dimensional histogram and fuzzy entropy principle’, IEEE Trans. Image Process., 2000, 9, (4), pp. 732735 (doi: 10.1109/83.841949).
    11. 11)
      • 11. Yoon, S.C., Lawrence, K.C., Park, B., Windham, W.R.: ‘Statistical model-based thresholding of multispectral images for contaminant detection on poultry carcasses’, Trans. Am. Soc. Agric. Biol. Eng. (ASABE), 2007, 50, (4), pp. 14331442.
    12. 12)
      • 12. Somasundaram, K., Kalavathi, P.: ‘Medical image binarization using square wave representation’. In: Balasubramaniam, P., (Ed.): ‘Control, computation and information systems volume 140 of communications in computer and information science’ (Springer, Berlin, Heidelberg, 2011), pp. 152158.
    13. 13)
      • 13. Hossain Shaikh, S., Kumar Maiti, A., Chaki, N.: ‘A new image binarization method using iterative partitioning’, Mach. Vis. Appl., 2012, 24, (2), pp. 337350 (doi: 10.1007/s00138-011-0402-4).
    14. 14)
      • 14. Woods, R.P., Cherry, S.R., Mazziotta, J.C.: ‘Rapid automated algorithm for aligning and reslicing pet images’, J. Comput. Assist. Tomogr., 1992, 16, (4), p. 6200 (doi: 10.1097/00004728-199207000-00024).
    15. 15)
      • 15. Woods, R.P., Mazziotta, J.C.: ‘MRI-PET registration with automated algorithm’, J. Comput. Assist. Tomogr., 1993, 17, (4), p. 536 (doi: 10.1097/00004728-199307000-00004).
    16. 16)
      • 16. Maintz, J.B.A., Viergever, M.A.: ‘A survey of medical image registration’, Med. Image Anal., 1998, 2, (1), pp. 136 (doi: 10.1016/S1361-8415(01)80026-8).
    17. 17)
      • 17. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: ‘Mutual-information-based registration of medical images: a survey’, IEEE Trans. Med. Imaging, 2003, 22, (8), pp. 9861004 (doi: 10.1109/TMI.2003.815867).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2012.0135
Loading

Related content

content/journals/10.1049/iet-cvi.2012.0135
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address