Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging

The purpose of this study is an application of scale invariant feature transform (SIFT) algorithm to stitch the cervical-thoracic-lumbar (C-T-L) spine magnetic resonance (MR) images to provide a view of the entire spine in a single image. All MR images were acquired with fast spin echo (FSE) pulse sequence using two MR scanners (1.5 T and 3.0 T). The stitching procedures for each part of spine MR image were performed and implemented on a graphic user interface (GUI) configuration. Moreover, the stitching process is performed in two categories; manual point-to-point (mPTP) selection that performed by user specified corresponding matching points, and automated point-to-point (aPTP) selection that performed by SIFT algorithm. The stitched images using SIFT algorithm showed fine registered results and quantitatively acquired values also indicated little errors compared with commercially mounted stitching algorithm in MRI systems. Our study presented a preliminary validation of the SIFT algorithm application to MRI spine images, and the results indicated that the proposed approach can be performed well for the improvement of diagnosis. We believe that our approach can be helpful for the clinical application and extension of other medical imaging modalities for image stitching.

[1]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[2]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

[3]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[4]  Xiaoping P. Hu,et al.  Diffusion tensor imaging reveals regional differences in the cervical spinal cord in amyotrophic lateral sclerosis , 2010, NeuroImage.

[5]  Bhakti Baheti,et al.  A novel approach for Automatic Image Stitching of spinal cord MRI images using SIFT , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[6]  Defeng Wang,et al.  Fully automatic stitching of diffusion tensor images in spinal cord , 2012, Journal of Neuroscience Methods.

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  Oleh Dzyubachyk,et al.  Automated algorithm for reconstruction of the complete spine from multistation 7T MR data , 2013, Magnetic resonance in medicine.

[9]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[10]  Khizar Hayat,et al.  Bilateral Symmetry Detection on the Basis of Scale Invariant Feature Transform , 2014, PloS one.

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Gregory C Sharp,et al.  Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication , 2013, Physics in medicine and biology.

[13]  Nuo Xu,et al.  Research of Two Steps-Optimization of SIFT Algorithm , 2013 .

[14]  Marco Riboldi,et al.  Quantification of organ motion based on an adaptive image-based scale invariant feature method. , 2013, Medical physics.

[15]  Mohammad Reza Daliri,et al.  Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images , 2012, Journal of Medical Systems.

[16]  Jie Tian,et al.  Rapid Multi-modality preRegistration based on SIFT descriptor , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Jun Zhu,et al.  Image Mosaic Method Based on SIFT Features of Line Segment , 2014, Comput. Math. Methods Medicine.

[18]  Jie Zhao,et al.  SIFT algorithm-based 3D pose estimation of femur. , 2014, Bio-medical materials and engineering.

[19]  Trevor Andrews,et al.  Magnetic resonance diffusion tensor imaging and tractography of the lower spinal cord: application to diastematomyelia and tethered cord , 2010, European Radiology.

[20]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Marco Riboldi,et al.  Scale Invariant Feature Transform as feature tracking method in 4D imaging: A feasibility study , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Ghassan Hamarneh,et al.  n -SIFT: n -Dimensional Scale Invariant Feature Transform , 2009, IEEE Trans. Image Process..