Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia

Olfa Ben Ahmed, Mohamed-Chacker Larabi, Marc Paccalin, Christine Fernandez-Maloigne


Visual assessment of brain atrophy for brain diseases diagnosis by clinicians is the most widely adopted method in clinical practices. Such a visually extracted knowledge represents a great potential to develop better training programs and create new tools to assist clinical decision making. Inspired by the clinician visual behavior, we propose in this work a new and automatic approach to detect and quantify local brain atrophies. The proposed approach combines both bottom-up and top-down visual saliency using domain knowledge in the brain MRI analysis. The first subsystem relies on low-level MRI characterization (texture and edge) while the second is based on an embedded learning process to identify and localize the subset of gray matter regions that provides optimal discrimination between subjects. The proposed method validated for the task of Alzheimer’s disease (AD) subjects recognition. Classification experiments were conducted on a subset of 188 anatomical MR images extracted from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI) dataset. We report accuracy of 81.48% and 76.66% respectively for AD versus Normal Control (NC) and Mild Cognitive Impairment (MCI) versus NC classification tasks.


  1. Agrawal, P., Vatsa, M., and Singh, R. (2014). Saliency based mass detection from screening mammograms. Signal Processing, 99:29 - 47.
  2. Ashburner, J. and Friston, K. J. (2000). Voxel-based morphometrythe methods. NeuroImage, 11(6):805-821.
  3. Banerjee, S., Mitra, S., Shankar, B. U., and Hayashi, Y. (2016). A novel gbm saliency detection model using multi-channel mri. PloS one, 11(1).
  4. Blennow, K., Hampel, H., Weiner, M., and Zetterberg, H. (2010). Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nature Reviews Neurology, 6(3):131-144.
  5. Braak, H. and Braak, E. (1998). Evolution of neuronal changes in the course of Alzheimer's disease. Neurology, 53:127-140.
  6. Chung, A. G., Scharfenberger, C., Khalvati, F., Wong, A., and Haider, M. A. (2015). Image Analysis and Recognition: 12th International Conference, ICIAR 2015, Niagara Falls, ON, Canada, July 22-24, 2015, Proceedings, chapter Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection, pages 368-376. Springer International Publishing, Cham.
  7. Frisoni, G. B., Testa, C., Sabattoli, F., Beltramello, A., Soininen, H., and Laakso, M. P. (2005). Structural correlates of early and late onset Alzheimers disease: voxel based morphometric study. Journal of Neurology, Neurosurgery and Psychiatry, 76(1):112-114.
  8. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389-422.
  9. Harel, J., Koch, C., and Perona, P. (2006). Graph-based visual saliency. In Advances in neural information processing systems, pages 545-552.
  10. Harper, L., Fumagalli, G. G., Barkhof, F., Scheltens, P., O'Brien, J. T., Bouwman, F., Burton, E. J., Rohrer, J. D., Fox, N. C., Ridgway, G. R., and Schott, J. M. (2016). Mri visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases. Brain.
  11. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B. (1998). Support vector machines. Intelligent Systems and their Applications, IEEE, 13(4):18-28.
  12. Itti, L., Koch, C., Niebur, E., et al. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence, 20(11):1254-1259.
  13. Jampani, V., Sivaswamy, J., Vaidya, V., et al. (2012). Assessment of computational visual attention models on medical images. In Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, page 80. ACM.
  14. Lala, D. and Nakazawa, A. (2016). Heat map visualization of multi-slice medical images through correspondence matching of video frames. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, ETRA 7816, pages 119-122, New York, NY, USA. ACM.
  15. Li, R., Shi, P., and Haake, A. R. (2013). Image understanding from experts' eyes by modeling perceptual skill of diagnostic reasoning processes. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 2187-2194.
  16. Ma, L., Wang, W., Zou, S., and Zhang, J. (2009). Liver focus detections based on visual attention model. In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, pages 1-5.
  17. Mahapatra, D. and Buhmann, J. M. (2015). Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings, chapter Visual Saliency Based Active Learning for Prostate MRI Segmentation, pages 9-16. Springer International Publishing, Cham.
  18. Mehmood, I., Baik, R., and Baik, S. W. (2013a). Automatic Segmentation of Region of Interests in MR Images Using Saliency Information and Active Contours, pages 537-544. Springer Netherlands, Dordrecht.
  19. Mehmood, I., Ejaz, N., Sajjad, M., and Baik, S. W. (2013b). Prioritization of brain {MRI} volumes using medical image perception model and tumor region segmentation. Computers in Biology and Medicine, 43(10):1471 - 1483.
  20. Nodine, C. F. and Kundel, H. L. (1987). Using eye movements to study visual search and to improve tumor detection. Radiographics, 7(6):1241-1250.
  21. Pulido, A., Rueda, A., and Romero, E. (2013). Classification of alzheimer's disease using regional saliency maps from brain mr volumes. In SPIE Medical Imaging, pages 86700R-86700R. International Society for Optics and Photonics.
  22. Pulidoa, A., Rueda, A., and Romeroa, E. Extracting regional brain patterns for classification of neurodegenerative diseases. In Proc. of SPIE Vol, volume 8922, pages 892208-1.
  23. Rueda, A., Gonzalez, F., Romero, E., et al. (2014). Extracting salient brain patterns for imaging-based classification of neurodegenerative diseases. Medical Imaging, IEEE Transactions on, 33(6):1262-1274.
  24. Shao, H., Zhang, Y., Xian, M., Cheng, H. D., Xu, F., and Ding, J. (2015). A saliency model for automated tumor detection in breast ultrasound images. In Image Processing (ICIP), 2015 IEEE International Conference on, pages 1424-1428.
  25. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. (2002). Automated anatomical labeling of activations in {SPM} using a macroscopic anatomical parcellation of the {MNI} {MRI} single-subject brain. NeuroImage, 15(1):273 - 289.
  26. Wen, G., Aizenman, A., Drew, T., Wolfe, J. M., Haygood, T. M., and Markey, M. K. (2016). Computational assessment of visual search strategies in volumetric medical images. Journal of Medical Imaging, 3(1):015501-015501.
  27. Yuan, Y., Wang, J., Li, B., and Meng, M. Q. H. (2015). Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Transactions on Medical Imaging, 34(10):2046-2057.
  28. Zou, X., Zhao, X., Yang, Y., and Li, N. (2016). Learningbased visual saliency model for detecting diabetic macular edema in retinal image. Computational Intelligence and Neuroscience, 2016.

Paper Citation

in Harvard Style

Ben Ahmed O., Larabi M., Paccalin M. and Fernandez-Maloigne C. (2017). Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 140-147. DOI: 10.5220/0006293001400147

in Bibtex Style

author={Olfa Ben Ahmed and Mohamed-Chacker Larabi and Marc Paccalin and Christine Fernandez-Maloigne},
title={Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia
SN - 978-989-758-215-8
AU - Ben Ahmed O.
AU - Larabi M.
AU - Paccalin M.
AU - Fernandez-Maloigne C.
PY - 2017
SP - 140
EP - 147
DO - 10.5220/0006293001400147