History and biomedical applications of digital signal and image processing

The paper presents in its initial part historical notes to the development of digital signal and image processing methods. The following case studies are devoted to their application in biomedicine and they include the use of computational intelligence in EEG and EMG signal processing, image segmentation and registration in orthodontia, the human-machine interaction and the three-dimensional modelling using MS Kinect in diagnostics and treatment of human motion disorders and neurological diseases. Associated comments include remarks and references to the development of modern computational tools, biosensors, wireless communication and data fusion used in assistive technologies and robotic systems. Mathematical tools common to all these applications form the next part of the paper that includes notes to spectral analysis, functional transforms, digital filtering, image enhancement, classification of multi-dimensional signal components and optimization using neural networks. Final remarks emphasize the interdisciplinary significance of the digital signal processing forming the integrating basis of many diverse research areas.

[1]  J. Kukal,et al.  Lag Synchronisation in the Human Brain: Evidence from 17,722 Healthy Subjects' EEG Analyses , 2014 .

[2]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[3]  Ales Procházka,et al.  Remote physiological and GPS data processing in evaluation of physical activities , 2013, Medical & Biological Engineering & Computing.

[4]  Aleš Procházka,et al.  Age-dependent complex noise fluctuations in the brain , 2013, Physiological measurement.

[5]  Ales Procházka,et al.  Change in the Characteristics of EEG Color Noise in Alzheimer’s Disease , 2014, Clinical EEG and neuroscience.

[6]  A. Prochazka,et al.  Age-Related Changes in the Energy and Spectral Composition of EEG , 2012, Neurophysiology.

[7]  Mohammadreza Yadollahi,et al.  Evaluation of dental morphometrics during the orthodontic treatment , 2014, Biomedical engineering online.

[8]  Tamal Bose,et al.  Digital Signal and Image Processing , 2003 .

[9]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[10]  Ales Procházka,et al.  Discrimination of axonal neuropathy using sensitivity and specificity statistical measures , 2014, Neural Computing and Applications.

[11]  Philosophical Essays , 1997, Nature.

[12]  Martin Kyncl,et al.  The diagnostic value of MRI fistulogram and MRI distal colostogram in patients with anorectal malformations. , 2013, Journal of pediatric surgery.

[13]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[14]  O. Vysata,et al.  Age-related changes in EEG coherence. , 2014, Neurologia i neurochirurgia polska.

[15]  Hugo Ferreira,et al.  Hybrid Brain Computer Interface Based on Gaming Technology: An Approach with Emotiv EEG and Microsoft Kinect , 2014 .

[16]  Yong Man Ro,et al.  Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms , 2013, Biomedical engineering online.

[17]  Ales Procházka,et al.  The use of combined illumination in segmentation of orthodontic bodies , 2015, Signal Image Video Process..

[18]  Tatjana Dostalova,et al.  Possibility of reconstruction of dental plaster cast from 3D digital study models , 2013, Biomedical engineering online.

[19]  Ales Procházka,et al.  The MS kinect image and depth sensors use for gait features detection , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[20]  O. Vysata,et al.  Age delays the recovery of distal motor latency after carpal tunnel syndrome surgery , 2014, Acta Neurochirurgica.