Abstract
Computer-aided diagnosis (CAD) systems have been the focus of many research endeavors. We consider the problem of building a CAD system for diagnosing lumbar disk herniation from MRI axial scans. Like other typical image based CAD systems, the CAD system we consider consists of several stages: image acquisition, region of interest (ROI) extraction and enhancement, feature extraction, and classification. Experimentally, we found that the ROI extraction is the hardest stage and it greatly determines the accuracy of the CAD system. In this work, we enhance on the ROI extraction process by using SIFT features, which are well known for their use in matching objects. The experiments conducted to evaluate the SIFT based ROI extraction approach shows its superiority over existing heuristic approach.
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Alawneh K, Al-dwiekat M, Alsmirat M, Al-Ayyoub M (2015) Computer-aided diagnosis of lumbar disc herniation. In: 2015 6th International Conference on Information and Communication Systems (ICICS), pp 286–291. https://doi.org/10.1109/IACS.2015.7103190
Al-Darabsah K, Al-Ayyoub M (2013) Breast cancer diagnosis using machine learning based on statistical and texture features extraction. In: Proceedings of the 4th international conference on information and communication systems (ICICS 2013), pp 53–57
Alomari R, Corso J, Chaudhary V, Dhillon G (2009) Abnormality detection in lumbar disks from clinical MRI images with a probabilistic model. In: Proceedings of 23rd international congress and exhibition on computer assisted radiology and surgery (CARS 2009), p 3
Alomari RS, Corso JJ, Chaudhary V, Dhillon G (2010) Automatic diagnosis of lumbar disk herniation using shape and appearance features from MRI. In: Proceedings of SPIE Conference on Medical Imaging. http://web.eecs.umich.edu/~jjcorso/pubs/alomari_spie2010.pdf
Alomari RS, Corso JJ, Chaudhary V, Dhillon G (2011a) Toward a clinical lumbar CAD: herniation diagnosis. Int J Comput Aided Radiol Surg 6(1):119–126. http://web.eecs.umich.edu/~jjcorso/pubs/alomari_ijcars2011.pdf
Alomari RS, Chaudhary V, Dhillon G (2011b) Computer aided diagnosis system for lumbar spine. In: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL ’11. ACM, New York, pp 145:1–145:5. https://doi.org/10.1145/2093698.2093843
Alomari RS, Corso JJ, Chaudhary V, Dhillon G (2013) Lumbar spine disk herniation diagnosis with a joint shape model. In: Proceedings of Medical Image Computing and Computer Aided Intervention Workshop on Computational Spine Imaging. http://www.cse.buffalo.edu/~jcorso/pubs/alomari_miccaiw2013.pdf
Alsmirat M, Alawneh K, Al-Ayyoub M, Al-dwiekat M (2017) Building a simulated educational environment for the diagnosis of lumbar disk herniation using axial view MRI scans. Int J Adv Intell Paradig (IJAIP) (To appear)
Bounds D, Lloyd P, Mathew B, Waddell G (1988) A multilayer perceptron network for the diagnosis of low back pain. In: Neural Networks. IEEE International Conference on, vol 2, pp 481–489. https://doi.org/10.1109/ICNN.1988.23963
Brown M, Lowe DG (2002) Invariant features from interest point groups. In: Proceedings of the British machine vision conference, pp1–23. https://doi.org/10.5244/C.16.23
Bu Y (2017) Research on the application of image processing technology based on sift features extraction in the retrieval and classification of art works. Revista de la Facultad de Ingeniería 32(6):162–170
Chang X, Yang Y (2017) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst 28:2294–2305
Corso JJ, Alomari RS, Chaudhary V (2008) Lumbar disk localization and labeling with a probabilistic model on both pixel and object features. In: Medical image computing and computer-assisted intervention–MICCAI. Springer, New York, pp 202–210. https://doi.org/10.1007/978-3-540-85988-8_25
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
Ghosh S, Alomari R, Chaudhary V, Dhillon G (2011a) Composite features for automatic diagnosis of intervertebral disk herniation from lumbar MRI. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 5068–5071. https://doi.org/10.1109/IEMBS.2011.6091255
Ghosh S, Alomari R, Chaudhary V, Dhillon G (2011b) Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In: IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp 1179–1182. https://doi.org/10.1109/ISBI.2011.5872612
Ghosh S, Malgireddy M, Chaudhary V, Dhillon G, (2012) A new approach to automatic disk localization in clinical lumbar MRI: combining machine learning with heuristics. In 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp 114–117. https://doi.org/10.1109/ISBI.2012.6235497
Hsieh MS, Tsai MD (2006) Three-dimensional surgery simulation system. US Patent 7,121,832
Keum JS, Lee HS, Hagiwara M (2012) Mean shift-based sift keypoint filtering for region-of-interest determination. In: 2012 Joint 6th international conference on soft computing and intelligent systems (SCIS) and 13th international symposium on advanced intelligent systems (ISIS). IEEE, pp 266–271. https://doi.org/10.1109/SCIS-ISIS.2012.6505144
Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, vol 2. IEEE, pp 1150–1157. https://doi.org/10.1109/ICCV.1999.790410
Lowe DG (2001) Local feature view clustering for 3d object recognition. In: Computer Vision and Pattern Recognition. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol 1. IEEE, pp I–I
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Ma J, Li Y, Wang QL (2012) Target recognition and features extractionbased on roi and sift descriptors. In: Applied mechanics and materials, vol 220. Trans Tech Publications, pp 1153–1157. https://doi.org/10.4028/www.scientific.net/AMM.220-223.1153
Michopoulou S, Costaridou L, Panagiotopoulos E, Speller R, Panayiotakis G, Todd-Pokropek A (2009) Atlas-based segmentation of degenerated lumbar intervertebral disks from mr images of the spine. IEEE Trans Biomed Eng 56(9):2225–2231. https://doi.org/10.1109/TBME.2009.2019765
Tsai MD, Jou SB, Hsieh MS (2002) A new method for lumbar herniated inter-vertebral disk diagnosis based on image analysis of transverse sections. Comput Med Imaging Graph 29(6):369–380. https://doi.org/10.1016/S0895-6111(02)00033-2
Vaughn M (2000) Using an artificial neural network to assist orthopaedic surgeons in the diagnosis of low back pain. http://www.marilyn-vaughn.co.uk/lbpainresearchstudy.htm
Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM international conference on multimedia. ACM, pp 1469–1472. https://doi.org/10.1145/1873951.1874249
Zhang C, Wang X, Feng J, Cheng Y, Guo C (2017) A car-face region-based image retrieval method with attention of sift features. Multimedia Tools Appl 76(8):10939–10958
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This work is funded in parts by the Deanship of Research at the Jordan University of Science and Technology with the grant number 20150310.
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Al-Ayyoub, M., Al-Mnayyis, N., Alsmirat, M.A. et al. SIFT based ROI extraction for lumbar disk herniation CAD system from MRI axial scans. J Ambient Intell Human Comput (2018). https://doi.org/10.1007/s12652-018-0750-2
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DOI: https://doi.org/10.1007/s12652-018-0750-2