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digital visualization of the biofield
Project leader: Dr. Robert Leskovar
Brief theoretic background
Peculiar differences in light oscillations in time
Research of the endogenous EM field and its visualization
Our experimental system
Methods
Experimental results
Conclusions
References
Brief theoretic background
Fröhlich proposed that collective modes of both electromechanical oscillations (phonons) and electromagnetic radiations (photons) extend over macroscopic distances within the organism and perhaps also outside the organism [1]. This theory has been extended by a number of theoretical physicists (e.g. Vitiello, Giudice, Duffield), who show that such coherent excitations can arise under the most general conditions of energy processing (pumping and sharing), and that once established, they are stably maintained [2].
Czech group of scientists, lead by Pokorny, have been measuring radiations from the EEMF within organisms, which confirm Fröhlich's predictions, although at somewhat lower frequencies [3].
Popp is one of the pioneers in detecting ultra-weak photon emission from living systems. He and many others since, have found that all organisms emit light ('biophotons') at ultra-weak intensities, which are strongly correlated with the cell cycle and other functional states [4].
From the research of biophotons hypotheses are proposed that the photons are held in a coherent form in the organism, and when stimulated, they are emitted coherently. Many experimental evidence confirm these hypotheses.
Dr. Victor Inyushin at Kazakh University in Russia suggests the existence of a so-called bioplasmic energy field composed of ions, free protons and free electrons.
His observations showed the bioplasmic particles are constantly renewed by chemical processes in the cells and are in constant motion. There appears to be a balance of positive and negative particles within the bioplasma that is relatively stable.
In spite of the normal stability of the bioplasma, Inyushin has found that a significant amount of this energy is radiated into space. According to him, clouds of bioplasmic particles, which have broken away from the organism, can be measured moving through the air.
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Peculiar differences in light oscillations in time
Researchers in the field of biophotonics have discovered that an organism emits - internally and externally - ultraweak photons.
Inyushin has detected plasma particle clouds in the proximity of an organism.
In accordance to these research approaches, from our experiments it can be inferred that the EEMF of the biological system influences, with some sort of a near-field effect, the environmental light particles [5][6][7]. The reach of this influence was observed to be from a few mm to 10 cm from the organism. Under this influence the environmental photons (coming into the camera from the background) appear to be dynamically merged or attenuated into forms that are often related to the shape of an organism, although they should normally follow random noise patterns.
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Research of the endogenous EM field (also "the biological field", "biofield" or "the bioplasmic field") and its visualization
Some other research and developmental projects are aimed towards measuring EM field in the proximity of an organism, or capture the corona discharge at the contact with the plate under a high voltage (a well known and fairly succesful Kirlian photography), some other measure current, frequency or voltage on a surface of an organism.
Most of these systems are already used for subsidiary diagnostics.
When applied for this purpose such systems (e.g. the system developed by Korotkov on the basis of Kirlian photography) relate measured values to the database of correlations, which stores correlations between energy states of a bigger group of tested subjects and their measured values. On this basis new data is extrapolated and visualized as two dimensional biological field around predefined shape of an organism but it is clear that this is not the actual biological field. Correlations that are used for this are found to correspond to organism's state in many times, but not always, because of insufficient knowledge of the biological field and its dynamics.
Measuring currents, resistances, frequencies or voltage, and extrapolation of the results.
Biophotonics
Analysis of Kirlian images (corona discharge from a contact with a plate under a high voltage)
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Our experimental system
The digital scientific camera DBK-21F04, Bayer colour 1/4" Sony CCD, firewire (made by The Imaging Source).
Lens: Cosmicar / Pentax C2514-M, 25 mm focal length; Pentax/Cosmicar TS2V314a zoom, 3.5-8mm focal length.
Mobile workstation Acer Inspire 1700, 1GB RAM, Pentium 4 2.6GHz, 17" display + external hard drive WD 160GB, firewire, for real-time capturing of the data.
Various sources of light: 100 Hz AC, or DC powered, different spectra, from 370nm UV to red
Custom developed computer algorithms and applications for video frame capturing and real-time or after-capture processing
Custom developed computer algorithms and applications for image processing and analysis
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Methods
Primary methods for detecting light oscillations are based on two basic approaches:
The Differential Contrast Photography (DCP) Method
For detecting differences of lightness in pixels with the same coordinates in successive images. If f(x, y) is the lightness of the image pixel with coordinates (x, y) then:
The differences (represented as real or absolute values) in pairs of successive (in time) images can be integrated and thus we get the amount of oscillations in certain time intervals.
We have developed many further variations of the basic method:
- processing of data in different image channels and color spaces (Red, Green, Blue, Hue, Saturation, Intensity),
- integration of differences from the running interval of images (e.g. images 1 to 10, 2 to 11, ...),
- integration of differences between the current image and the fixed image (or fixed integration of images, e.g. differences between images I2-I1, I3-I1, I4-I1, ..., or images I10 - 1/5 *
 I, I11 - 1/5 * I, ...), etc.
- integration of blurred differences with intention to spread the signal and reduce the random noise.
The Gradient Method
For detection of edges of subtle light oscillation structures with the gradient operator, with help of which it is possible to amplify the structures according to the magnitude or the direction of the gradients of lightness.
If f(x, y) is lightness of the image pixel with coordinates (x, y) then:
Gradient magnitude images:
 
Gradient direction images:
Methods for texture analysis of oscillations:
enable us to evaluate their statistical properties (e.g. standard deviation, contrast, homogeneity, entropy). [Textures are, generally speaking, regions where a certain pattern or patterns repeat.] Texture analysis is peformed in a running window in an original image. Each pixel in the resulting image has the value of the analysis according to a certain statistics calculated in the window.
Gradient image (a leaf in the test tube) |
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| Standard deviation (window size 8x8 pix.) |
Entropy (window size 8x8 pix.) |
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1st row: Captured, successive images (weak DC stabilized lighting)
2nd row: Lightness gradient magnitudes (red, green, blue channels) |
| Last row: Entropy of a running window in images of gradient magnitudes |
Magnified: Gradient entropy image showing a distinct area around the leaf |
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Experimental results
Differential Contrast Photography (DCP) Method
Differences of pixel lightness (green channel, integration of 15 differences (=30 frames=1s of time) with a Gaussian blur and a contrast applied). Circular-shaped and streamer-like clusters of pixels can be seen on images where the apple was cut.
 
 
 
Gradient method
Integration of lightness gradient magnitudes (red, green, blue channels).
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Conclusions according to the current phase of research
Weak light oscillations detected in the proximity of organisms and in time are found to be different than those in the distance.
Oscillations do not appear to be periodical, neither according to their location nor according to the time, so their structure (e.g. statistical moments) and time of appearance are not predictable, yet. At this time they do not represent the signal poweful enough to be able to easily distinguish it from the photon noise and other sources of random noise.
The detected spatial light oscillations often form streamers that show a resemblance to the Kirlian plasmic corona streamers. This may be further evidence of the existence of the so-called bioplasma.
Our next steps will involve research of oscillations in narrow spectral bands from UV to IR spectra with a more sensitive optical equipment. Also further methods for image processing and analysis will be developed.
If you would be willing to support this and/or other research projects of our Institute
please take a look at this page.
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References:
[1] H. Fröhlich. The biological effects of microwaves and related questions. Adv. Electronics and Electron. Phys. 53. p. 85-152. 1980.
[2] N.G. Duffield. Global stability of condensation in the continuum limit for Frohlich's pumped phonon system. J. of Physics. 21. p. 625-641. 1988.
[3] J. Pokorny, J. Ha¹ek, F. Jelinek, J. ©aroch, B. Palan. Electromagnetic activity of yeast in the M phase. Electr. Magnetobiol. 20(3). p. 371-396. 2002.
[4] F.A. Popp, K.H. Li, Q. Gu. (Eds). Recent Advances in Biophoton Research, World Scientific, Singapore, 1992.
[5] R.T. Leskovar, M. ©karja, I. Jerman. Detection of biofield - ambient light interactions. Mind body studies. Cognitive science. (eds. Kononenko I, Jerman I). Proceedings of the 6th International Multi Confernce. Information Society IS 2003. Ljubljana, Slovenia, p. 12-15, 2003.
[6] R.T. Leskovar, M. ©karja, I. Jerman. Photographing biofields. The 13th Orkney International Science Festival, Orkney, 2003.
[7] M. ©karja, I. Jerman, R. Ru¾iè. Some evidence that organisms' endogenous field may influence ambient light (preliminary report). International Symposium Endogeneous Physical Fields in Biology, Prague, Czech Republic. p. 74-75, 2002.
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