Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation

Multi-spectral computed tomography is an emerging technology for the non-destructive identification of object materials and the study of their physical properties. Applications of this technology can be found in various scientific and industrial contexts, such as luggage scanning at airports. Material distinction and its identification is challenging, even with spectral x-ray information, due to acquisition noise, tomographic reconstruction artefacts and scanning setup application constraints. We present MUSIC - and open access multi-spectral CT dataset in 2D and 3D - to promote further research in the area of material identification. We demonstrate the value of this dataset on the image analysis challenge of object segmentation purely based on the spectral response of its composing materials. In this context, we compare the segmentation accuracy of fast adaptive mean shift (FAMS) and unconstrained graph cuts on both datasets. We further discuss the impact of reconstruction artefacts and segmentation controls on the achievable results. Dataset, related software packages and further documentation are made available to the imaging community in an open-access manner to promote further data-driven research on the subject

[1]  R. Leahy,et al.  Digimouse: a 3D whole body mouse atlas from CT and cryosection data , 2007, Physics in medicine and biology.

[2]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Andre Mouton,et al.  A review of automated image understanding within 3D baggage computed tomography security screening. , 2015, Journal of X-ray science and technology.

[4]  A. Journel,et al.  Reservoir Modeling Using Multiple-Point Statistics , 2001 .

[5]  Robert Feidenhans'l,et al.  Spectral correction algorithm for multispectral CdTe x-ray detectors , 2018 .

[6]  Toby P. Breckon,et al.  Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[7]  Krishna Mohan Buddhiraju,et al.  Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modified Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[9]  Julia F. Barrett,et al.  Artifacts in CT: recognition and avoidance. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[10]  Camille Couprie,et al.  Power Watershed: A Unifying Graph-Based Optimization Framework , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Jan Kehres,et al.  Detector response artefacts in spectral reconstruction , 2017, Optical Engineering + Applications.

[13]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.

[14]  Ilan Shimshoni,et al.  Mean shift based clustering in high dimensions: a texture classification example , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[16]  Muhammet Bastan,et al.  Visual Words on Baggage X-Ray Images , 2011, CAIP.

[17]  Thomas Flohr,et al.  Image Fusion in Dual Energy Computed Tomography: Effect on Contrast Enhancement, Signal-to-Noise Ratio and Image Quality in Computed Tomography Angiography , 2009, Investigative radiology.

[18]  Ronald J. Jaszczak,et al.  Physical Factors Affecting Quantitative Measurements Using Camera-Based Single Photon Emission Computed Tomography (Spect) , 1981, IEEE Transactions on Nuclear Science.

[19]  Syed Zulqarnain Gilani,et al.  A Clustering Based Automated Glacier Segmentation Scheme Using Digital Elevation Model , 2009, 2009 Digital Image Computing: Techniques and Applications.

[20]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..

[22]  Trevor J. Hastie,et al.  Sparse Discriminant Analysis , 2011, Technometrics.

[23]  Charl P. Botha,et al.  Hybrid Scheduling in the DeVIDE Dataflow Visualisation Environment , 2008, SimVis.

[24]  Sameer Singh,et al.  Image segmentation optimisation for X-ray images of airline luggage , 2004, Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2004. CIHSPS 2004..

[25]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[26]  C. McCollough,et al.  Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. , 2015, Radiology.

[27]  Gal Chechik,et al.  Object separation in x-ray image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Seemeen Karimi,et al.  Metal artifact reduction for CT-based luggage screening , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Jan Kehres,et al.  Threat detection of liquid explosives and precursors from their x-ray scattering pattern using energy dispersive detector technology , 2017, Optical Engineering + Applications.

[30]  P. Hansen.  Discrete Inverse Problems: Insight and Algorithms , 2010 .

[31]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[32]  F O Bochud,et al.  Image quality in CT: From physical measurements to model observers. , 2015, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[33]  W. Clem Karl,et al.  Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security , 2015, IEEE Transactions on Image Processing.

[34]  W. Clem Karl,et al.  Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography , 2011, Electronic Imaging.

[35]  B. Ersbøll,et al.  Analysis of micro-structure in raw and heat treated meat emulsions from multimodal X-ray microtomography , 2014 .

[36]  Toby P. Breckon,et al.  Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery , 2018, IEEE Transactions on Information Forensics and Security.

[37]  Sang Wook Lee,et al.  Spectral gradient: a material descriptor invariant to geometry and incident illumination , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[39]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[40]  Elli Angelopoulou,et al.  Mean-shift clustering for interactive multispectral image analysis , 2013, 2013 IEEE International Conference on Image Processing.

[41]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[42]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[43]  Trevor Darrell,et al.  Adaptive Mean Shift Based Clustering in High Dimensions , 2006 .

[44]  Toby P. Breckon,et al.  Materials-based 3D segmentation of unknown objects from dual-energy computed tomography imagery in baggage security screening , 2015, Pattern Recognit..

[45]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[46]  B.R. Abidi,et al.  Improving Weapon Detection in Single Energy X-Ray Images Through Pseudocoloring , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[47]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[48]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[49]  Hai Jin,et al.  Color Image Segmentation Based on Mean Shift and Normalized Cuts , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[50]  S. Wildermuth,et al.  Dual- and multi-energy CT: approach to functional imaging , 2011, Insights into imaging.

[51]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[52]  Samet Akcay,et al.  On using feature descriptors as visual words for object detection within X-ray baggage security screening , 2016, ICDP.

[53]  Toby P. Breckon,et al.  Investigating existing medical CT segmentation techniques within automated baggage and package inspection , 2013, Optics/Photonics in Security and Defence.

[54]  Zhuowen Tu,et al.  Image Segmentation by Data-Driven Markov Chain Monte Carlo , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

[56]  Guillermo Sapiro,et al.  Color Snakes , 1997, Comput. Vis. Image Underst..

[57]  Anders Bjorholm Dahl,et al.  Foreign Object Detection in Multispectral X-ray Images of Food Items Using Sparse Discriminant Analysis , 2017, SCIA.

[58]  A. C. Riddle,et al.  Inversion of Fan-Beam Scans in Radio Astronomy , 1967 .