Abstract
Object localization is an important step common to many different medical applications. In this Chapter, we will review the challenges and recent approaches tackling this problem, and focus on the work by Song et.al. [20]. In [20], a new graphical model with additional contrast and interest-region potentials is designed, encoding the higher-order contextual information between regions, on the global and structural levels. A discriminative sparse-coding based interest-region detector is also integrated as one of the context prior in the graphical model. This object localization method is generally applicable to different medical imaging applications, in which the objects can be distinguished from the background mainly based on feature differences. Successful applications on two different medical imaging applications – lesion dissimilarity on thoracic PET-CT images and cell segmentation on microscopic images – are demonstrated in the experimental results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(1), 4311–4322 (2006)
Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph Cuts with Invariant Object-Interaction Priors: Application to Intervertebral Disc Segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Cheng, L., Ye, N., Yu, W., Cheah, A.: Discriminative Segmentation of Microscopic Cellular Images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 637–644. Springer, Heidelberg (2011)
Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., Mori, K.: Mediastinal atlas creation from 3-d chest computed tomography images: application to automated detection and station mapping of lymph nodes. Med. Image Anal. 16(1), 63–74 (2011)
Gonfaus, J., Boix, X.: Harmony potentials for joint classification and segmentation. In: CVPR, pp. 3280–3287 (2010)
Guo, R., Dai, Q., Hoiem, D.: Single-image shadow detection and removal using paired regions. In: CVPR, pp. 2033–2040 (2011)
Jagadeesh, V., Vu, N., Manjunath, B.S.: Multiple Structure Tracing in 3D Electron Micrographs. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 613–620. Springer, Heidelberg (2011)
Jiang, Z., Lin, Z., Davis, L.: Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In: CVPR, pp. 1697–1704 (2011)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical CRFs for object class image segmentation. In: ICCV, pp. 739–746 (2009)
Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, Where and How Many? Combining Object Detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 424–437. Springer, Heidelberg (2010)
Lezoray, O., Cardot, H.: Cooperation of color pixel classification schemes and color watershed: a study for microscopical images. IEEE Trans. Image Process. 11(7), 783–789 (2002)
Liu, M., Lu, L., Ye, X., Yu, S., Salganicoff, M.: Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 41–48. Springer, Heidelberg (2011)
Lu, C., Chelikani, S., Jaffray, D.A., Milosevic, M.F., Staib, L.H., Juncan, J.S.: Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy. IEEE Trans. Med. Imag. 31(6), 1213–1227 (2012)
van Ravesteijin, V.F., van Wijk, C., Vos, F.M., Truyen, R., Peters, J.F., Stoker, J., van Vliet, L.J.: Computer-aided detection of polyps in CT colonography using logistic regression. IEEE Trans. Med. Imag. 29(1), 120–131 (2010)
Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
Song, Y., Cai, W., Eberl, S., Fulham, M.J., Feng, D.: Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 191–198. Springer, Heidelberg (2011)
Song, Y., Cai, W., Eberl, S., Fulham, M., Feng, D.: Thoracic image case retrieval with spatial and contextual information. In: ISBI, pp. 1885–1888 (2011)
Song, Y., Cai, W., Huang, H., Wang, Y., Feng, D.D.: Object localization in medical images based on graphical model with contrast and interest-region terms. In: CVPR Workshop, pp. 1–7 (2012)
Song, Y., Cai, W., Kim, J., Feng, D.D.: A multistage discriminative model for tumor and lymph node detection in thoracic images. IEEE Trans. Med. Imag. 31(5), 1061–1075 (2012)
Suykens, J., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Letters 9(3), 293–300 (1999)
Tropp, J.: Greed is good: algorithmic results for sparse approximation. IEEE Trans. Inf. Theory 50(10), 2231–2242 (2004)
Vedaldi, A., Soatto, S.: Quick Shift and Kernel Methods for Mode Seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)
Wu, D., Lu, L., Bi, J., Shinagawa, Y., Boyer, K., Krishnan, A., Salganicoff, M.: Stratified learning of local anatomical context for lung nodules in CT images. In: CVPR, pp. 2791–2798 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, Y., Cai, W., Huang, H., Wang, Y., Feng, D.D. (2013). Context Enhanced Graphical Model for Object Localization in Medical Images. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-36620-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36619-2
Online ISBN: 978-3-642-36620-8
eBook Packages: Computer ScienceComputer Science (R0)