Sensor, Signal and Image Informatics

Summary

Objectives

The number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.

Methods

Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.

Results

The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.

Conclusions

Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

 

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Keywords

Image Processing - Computer-Assisted - Signal Processing - Computer-Assisted - Diagnostic Imaging - Automatic Data Processing - Medical Informatics Applications

 

 

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• References

• 1 Lehmann TM, Meinzer HP, Tolxdorff T. Advances in biomedical image analysis – Past, present and future challenges. Methods Inf Med 2004; 43 (04) 308-14.
• 2 Linnenbrügger NI, Webber RL, Kobbelt LP, Lehmann TM. Automated hybrid TACT® volume reconstructions. Methods Inf Med 2004; 43 (04) 315-9.
• 3 Weber S, Schüle T, Schnörr C, Hornegger J. A linear programming approach to limited angle 3D reconstruction from DSA projections. Methods Inf Med 2004; 43 (04) 320-6.
• 4 Fischer G, Pfeifer B, Seger M, Hintermüller C, Hanser F, Modre R. Tilg et al. Computationally efficient noninvasive cardia activation imaging. Methods Inf Med 2005; 44 (05) 674-86.
• 5 Cerutti S, Bianchi AM, Mainardi LT. Advanced spectral methods for detecting dynamic behavior. Autonomic Neuroscience-Basic & Clinical 2001; 90: 3-12.
• 6 Winterhalder M, Schelter B, Hesse W, Schwab K, Leistritz L, Klan D. et al. Comparison of time series analysis techniques to detect direct and time-varying interrelations in multivariate, neural systems. Signal Processing Journal 2005; 85: 2137-60.
• 7 Ganesan R, Das TK, Venkataraman V. Waveletbased multiscale statistical process monitoring – A literature review. IIE Transactions on Quality and Reliability Engineering 2004; 36 (09) 787-806.
• 8 Cohen L. Time-frequency analysis. IEEE Signal Processing Magazine 1999; 16: 22-8.
• 9 Lehnertz K, Andrzejak RG, Arnhold J, Kreuz T, Mormann F, Rieke CWidman. et al. Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. J Clin Neurophysiol 2001; 18: 209-22.
• 10 Rapp PE, Watanabe TAA, Faure P, Cellucci CJ. Nonlinear signal classification. International Journal of Bifurcation and Chaos 2002; 12: 1273-93.
• 11 Mulert C, Jager L, Schmitt R, Bussfeld P, Pogarell O, Moller HJ. et al. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage 2004; 22: 83-94.
• 12 Xiao X, Mullen TJ, Mukkamala R. System identification: a multi-signal approach for probing neural cardiovascular regulation. Physiological Measurement 2005; 26: 41-71.
• 13 Coiera EW. Automated signal interpretation. In: Hutton RP. Monitoring in Anesthesia and Intensive Care. London: W. B. Saunders Co. Ltd; 1994: 32-42.
• 14 Celka P, Boashash B, Colditz P. Preprocessing and time-frequency analysis of newborn EEG seizures. IEEE Engineering in Medicine and Biology Magazine 2001; 20: 30-9.
• 15 Agarwal R, Gotman J. Long-term EEG compression for intensive-care settings. IEEE Eng Med Biol Mag 2001; 20: 23-9.
• 16 Thoraval L, Carrault G, Schleich JM, Summers R, van de Velde M, Diaz J. Data fusion of electrophysiological and haemodynamic signals for ventricular rhythm tracking. IEEE Eng Med Biol Mag 1997; 16: 48-55.
• 17 Stokking R, Zubal IG, Viergever MA. Display of fused images: Methods, interpretation, and diagnostic improvements. Semin Nucl Med 2003; 33: 219-27.
• 18 Lohscheller J, Dollinger M, Schuster M, Schwarz R, Eysholdt U, Hoppe U. Quantitative investigation of the vibration pattern of the substitute voice generator. IEEE Trans Biomed Eng 2004; 51: 1394-400.
• 19 Sintchenko V, Coiera EW. Which clinical decisions benefit from automation? A task complexity approach. Int J Med Inform 2003; 70: 309-16.
• 20 Coiera EW. Artificial intelligence in medicine: the challenges ahead. J Am Med Inform Assoc 1996; 03: 363-6.
• 21 Petersson KM, Nichols TE, Poline JB, Holmes AP. Statistical limitations in functional neuroimaging I. Non-inferential methods and statistical models. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 1999; 354: 1239-60.
• 22 Reichertz PL. Towards systematisation. Methods Inf Med 1977; 16 (03) 125-30.
• 23 Haux R. On medical informatics. Methods Inf Med 1989; 28: 66-8.
• 24 Kalender W. Computed Tomography. Fundamentals, System technology, Image Quality, Applications. New York: Wiley & Sons; 2001
• 25 Hsieh J. Computed Tomography. Principles, Design, Artifacts, and Recent Advances. SPIE Press; Bellingham: 2003
• 26 Becker CR, Ohnesorge BM, Schoepf UJ, Reiser MF. Current development of cardiac imaging with multidetector-row CT. Eur J Radiol 2000; 36: 97-103.
• 27 Imhof H, Schibany N, Ba-Ssalamah A, Czerny C, Hojreh A, Kainberger F. et al. Spiral CT and radiation dose. Eur J Radiol 2003; 47: 29-37.
• 28 Aach T, Schiebel U, Spekowius G. Digital image acquisition and processing in medical x-ray imaging. J Electron Imaging 1999; 08: 7-22.
• 29 Giacomuzzi SM, Erckert B, Freund MC, Schopf T, Dessl A, Jaschke W. Dose reduction in computerized tomography with a new scan procedure. Aktuelle Radiologie 1996; 06: 110-3.
• 30 Kopka L, Funke M, Breiter N, Hermann KP, Vosshenrich R, Grabbe E. An anatomically adapted variation of the tube current in CT – Studies on radiation dosage reduction and image quality. Fortschritte auf dem Gebiete der Röntgenstrahlen und der Nuklearmedizin 1995; 163 (05) 383-7.
• 31 Hundt W, Rust F, Stäbler A, Wolff H, Suess C, Reiser M. Dose reduction in multisclice computed tomography. J Comput Assist Tomogr 2005; 29 (01) 140-7.
• 32 Feldkamp LA, Davies LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A 1984; 01: 612-9.
• 33 Xiao S, Bresler Y, Munson Jr DC. Fast feldkamp algorithm for cone-beam computer tomography. Proceedings IEEE International Conference on Image Processing (ICIP) 2003; 819-22.
• 34 Spekowius G, Boerner H, Eckenbach W, Quadflieg P, Laurenssen GJ. Simulation of the imaging performance of x-ray image intensifier/ TV camera chains. Proceedings SPIE 1995; 2432: 12-23.
• 35 Rabbani M, Shaw R, van Mettler R. Detective quantum efficiency of imaging systems with amplifying and scattering mechanisms. Journal Opt Soc Am A 1987; 04 (05) 895-901.
• 36 Koppe R, Klotz E, op de Beek JO, Aerts H. 3D vessel reconstruction based on rotational angiography. Proceedings Computer Assisted Radiology (CAR) 1995; 101-7.
• 37 Busse F, Rütten W, Sandkamp B, Alving PL, Bastiaens R. Design and performance of a highquality cardiac flat panel detector. Proceedings SPIE 2002; 4682: 819-27.
• 38 Siewerdsen JH, Jaffray DA. Optimization of x-ray imaging geometry (with specific application to flatpanel cone-beam computed tomography). Med Phys 2000; 27: 1903-14.
• 39 Siewerdsen JH, Jaffray DA. Cone-beam computed tomography with a flat-panel imager: Magnitude and effects of x-ray scatter. Med Phys 2001; 28: 220-31.
• 40 Rose G, Wiegert J, Schaefer D, Fiedler K, Conrads N, Timmer J. et al. Image quality of flat panel conebeam CT. Proceedings SPIE 2003; 5030: 677-83.
• 41 Bertram M, Rose G, Schäfer D, Wiegert J, Aach T. Directional interpolation of sparsely sampled conebeam CT sinogram data. Proceedings IEEE International Symposium on Biomedical Imaging (ISBI). 2004: 928-31.
• 42 Bigün J, Granlund GH. Optimal orientation detection of linear symmetry. Proceedings IEEE International Conference on Computer Vision (ICCV). 1987: 433-8.
• 43 Di Zenzo S. A note on the gradient of a multiimage. Computer Vision, Graphics, and Image Processing 1986; 33: 116-25.
• 44 Lehmann TM, Gönner C, Spitzer K. Survey: Interpolation methods in medical image processing. IEEE Trans Med Imaging 1999; 18 (11) 1049-75.
• 45 Lehmann TM, Gönner C, Spitzer K. Addendum – B-spline interpolation in medical image processing. IEEE Trans Med Imaging 2001; 20 (07) 660-5.
• 46 Endo M, Tsunoo T, Nakamori N. Effect of scatter radiation on image noise in cone beam CT. Proceedings SPIE 2000; 3977: 514-21.
• 47 Wiegert J, Aach T, Rose G, Schaefer D, Bertram M, Conrads N. et al. Performance of standard fluoroscopy anti-scatter grids in flat panel based cone beam CT. Proceedings SPIE 2004; 5368: 67-78.
• 48 Siewerdsen JH, Mosley DJ, Bakhtiar B, Richard S, Jaffray DA. The influence of antiscatter grids on softtissue detectability in cone-beam computed tomography with flat-panel detectors. Med Phys 2004; 31: 3506-20.
• 49 Wiegert J, Bertram M, Rose G, Aach T. Model based scatter correction for cone-beam computed tomography. Proceedings SPIE 2005; 5745: 271-82.
• 50 Seidel G, Meyer K, Berdien G, Hollstein D, Toth D, Aach T. Ultrasound perfusion imaging in acute middle cerebral artery infarction predicts stroke outcome. Stroke 2004; 35: 1107-11.
• 51 Wei K, Jayaweera A, Firoonza S, Linka A, Skyba D, Kaul S. Quantification of myocardial blood flow with ultrasound-induced destruction of microbubbles administered as a constant venous infusion. Circulation 1998; 97: 473-83.
• 52 Schoelgens C. Native tissue harmonic imaging. Radiologe 1998; 05: 420-423.
• 53 Kier C, Toth D, Meyer-Wiethe K, Schindler A, Seidel G, Aach T. Cerebral perfusion imaging with bolus harmonic imaging. Proceedings SPIE 2005; 5750: 437-46.
• 54 Krakow K, Allen PJ, Lemieux L, Symms MR, Fish DR. Methodology: EEG-correlated fMRI. Adv Neurol 2000; 83: 187-201.
• 55 Tzabazis A, Ihmsen H, Schywalsky M, Schwilden H. EEG-controlled closed-loop dosing of propofol in rats. Br J Anaesth 2004; 92: 564-9.
• 56 Sinkjaer T, Haugland M, Inmann A, Hansen M, Nielsen KD. Biopotentials as command and feedback signals in functional electrical stimulation systems. Medical Eng Phys 2003; 25: 29-40.
• 57 Roco MC, Bainbridge WS. Converging technologies for improving human performance. Dordrecht: Kluwer Academic Publishers; 2003
• 58 Bach-y-Rita P, Kercel SW. Sensory substitution and the human-machine interface. Trends in Cognitive Sciences 2003; 07: 541-6.
• 59 del R, Millan J, Renkens F, Mourino J, Gerstner W. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng 2004; 51: 1026-31.
• 60 Santana D, Ramirez M, Ostrosky-Solis F. Recent advances in rehabilitation technology: A re-view of the brain-computer interface. Revista De Neurologia 2004; 39: 447-50.
• 61 Bruns A. Fourier-, Hilbert-and wavelet-based signal analysis: are they really different approaches?. J Neurosci Methods 2004; 137: 321-32.
• 62 Nie K, Stickney G, Zeng FG. Encoding frequency modulation to improve cochlear implant performance in noise. IEEE Trans Biomed Eng 2005; 52: 64-73.
• 63 Flanagan LJ, Golden RM. Phase vocoder. The Bell System Technical Journal 1966; 45: 1494-509.
• 64 Niblack W, Barber R, Equitz W, Flickner M, Yanker P, Ashley J. The QBIC-project – Querying images by content using color, texture and shape. Proceedings SPIE 1993; 1908: 173-87.
• 65 Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B. et al. Query by image and video content – The QBIC system. IEEE Computer 1995; 28 (09) 23-32.
• 66 Tagare HD, Jaffe CC, Duncan J. Medical image databases – A content-based retrieval approach. J Am Med Inform Assoc 1997; 04: 184-98.
• 67 Müller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medicine – Clinical benefits and future directions. Int J Med Inform 2004; 73: 1-23.
• 68 Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22 (12) 1349-80.
• 69 Lehmann TM, Güld MO, Thies C, Fischer B, Spitzer K, Keysers D. Ney et al. Content-based image retrieval in medical applications. Methods Inf Med 2004; 43 (04) 354-61.
• 70 Lehmann TM, Schubert H, Keysers D, Kohnen M, Wein BB. The IRMA code for unique classification of medical images. Proceedings SPIE 2003; 5033: 440-51.
• 71 Lehmann TM, Güld MO, Deselaers T, Keysers D, Schubert H, Spitzer K. et al. Automatic categorization of medical images for content-based retrieval and data mining. Computerized Medical Imaging and Graphics 2005; 29 (02) 143-52.
• 72 Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 03: 610-21.
• 73 Tamura H, Mori S, Yamawaki T. Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 1978; 08 (06) 460-72.
• 74 Güld MO, Keysers D, Leisten M, Schubert H, Lehmann TM. Comparison of global features for categorization of medical images. Proceedings SPIE 2004; 5371: 211-22.
• 75 Keysers D, Dahmen J, Ney H, Wein B, Lehmann TM. A statistical framework for model-based image retrieval in medical applications. J Electron Imaging 2003; 12 (01) 59-68.
• 76 Lehmann TM, Güld MO, Keysers D, Schubert H, Kohnen M, Wein BB. Determining the view position of chest radiographs. J Digit Imaging 2003; 16 (03) 280-91.
• 77 Pan B, Zhang D, Li N, Wang K. Computerized tongue diagnosis based on Bayesian networks. IEEE Trans Biomed Eng 2004; 51 (10) 1803-10.
• 78 Chiu CC, Lin HS, Lin SL. A structural texture recognition approach for medical diagnosis through tongue. Biomedical Engineering, Applications, Basis and Communications 1995; 07 (02) 143-8.
• 79 Chiu CC. A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue. Comput Methods Programs Biomed 2000; 61 (02) 77-89.
• 80 Li CH, Yuen PC. Tongue image matching using color content. Pattern Recognition 2002; 35 (02) 407-19.
• 81 Fischer B, Modersitzki J. Intensity based image registration with a guaranteed one-to-one point match. Methods Inf Med 2004; 43 (04) 327-30.
• 82 Bartz D, Orman J, Gürvic Ö. Accurate volumetric measurements of anatomic cavities. Methods Inf in Med 2004; 43 (04) 331-5.
• 83 Chrástek R, Skokan M, Kubecka L, Wolf M, Donath K, Jan J. et al. Multimodal retinal image registration for optic disk segmentation. Methods Inf Med 2004; 43 (04) 336-42.
• 84 Wittenberg T, Grobe M, Münzenmayer C, Kuziela H, Spinner K. A semantic approach to segmentation of overlapping objects. Methods Inf Med 2004; 43 (04) 343-53.
• 85 Imabayashi E, Matsuda H, Asada T, Ohnishi T, Sakamoto S, Nakano S. et al. Superiority of 3-dimensional stereotactic surface projection analysis over visual inspection in discrimination of patients with very early Alzheimer’s disease from controls using brain perfusion SPECT. J Nucl Med 2004; 45 (09) 1450-7.
• 86 Burdette JH, Minoshima S, Borght TV, Tran DD, Kuhl DE. Alzheimer Disease – Improved visual inspection of PET images by three-dimensional stereotaxic surface projections. Radiology 1996; 198: 837-43.
• 87 Minoshima S, Giordani B, Berent S. et al. Metabolic reduction in the posterior cingolate cortex in very early Alzheimer’s disease. Ann Neurol 1997; 42: 85-94.
• 88 Gerig G, Koller T, Szekely G, Brechbühler C, Kübler O. Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. Lecture Notes in Computer Science. 1993: 94-111.
• 89 Selle D, Preim B, Schenk A, Peitgen HO. Analysis of vaslulature for liver surgical planning. IEEE Transactions of Medical Imaging 2002; 21 (11) 1344-57.
• 90 Bloomenthal J, Shoemake K. Convolution surfaces. Computer Graphics 1991; 25 (04) 251-6.
• 91 Oeltze S, Preim B. Visualization of vasculature with convolution surfaces – Method, validation and evaluation. IEEE Trans Med Imaging 2005; 24 (04) 540-8.
• 92 Suzuki S, Suzuki N, Hattori A, Uchiyama A, Kobayashi S. Sphere-filled organ model for virtual surgery system. IEEE Trans Med Imaging 2004; 23 (06) 714-22.