Rapid determination of microbial growth and antimicrobial susceptibility

1. A method, comprising:

detecting, by a computer-based system in communication with a microorganism detection device, a first set of at least one of signals, spots, or singularities in a first image of a sample of an immobilized microorganism in a gel medium;

determining, by the computer-based system, that a first subset of the first set of the at least one of signals, spots, or singularities are greater than a predetermined threshold by employing a first wavelet transformation for decomposing the first set of the at least one of signals, spots or singularities into wavelet functions, applying the wavelet functions to the first image and generating wavelet coefficients representing the first image of the immobilized microorganism on a plurality of levels of resolution to create a stack of first image planes;

determining, by the computer-based system a first value associated with an attribute of the immobilized microorganism, based on first information associated with the first subset of the first set of at least one of signals, spots, or singularities that are greater than the predetermined threshold, wherein the attribute comprises at least one of a microorganism size, shape, mass, intrinsic fluorescence, cell surface feature membrane integrity, genomic DNA, ribosomal RNA subunits, or bound marker used in indirect immunofluorescence microscopy;

detecting, by the computer-based system in communication with the microorganism detection device, a second set of at least one of signals, spots, or singularities in a second image of the sample of an immobilized microorganism, after an occurrence of an event;

determining, by the computer-based system, that a second subset of the second set of the at least one of signals, spots, or singularities are greater than a predetermined threshold by employing a second wavelet transformation for decomposing the second set of the at least one of signals, spots, or singularities into wavelet functions, applying the wavelet functions to the second image and generating wavelet coefficients representing the second image of the immobilized microorganism on a plurality of levels of resolution to create a stack of second image planes;

determining, by the computer-based system, a second value associated with the attribute of the immobilized microorganism, based on second information associated with the second set of at least one of signals, spots, or singularities that are greater than the predetermined threshold after the occurrence of the event;

determining, by the computer-based system, a growth rate of the immobilized microorganism associated with the first value and the second value;

comparing, by the computer-based system, the growth rate to a control growth rate; determining, by the computer-based system, a clone signal intensity curve shape likelihood and a tracking error likelihood; and

calculating, by the computer-based system, a growth likelihood value based on the clone signal intensity curve shape likelihood and the tracking error likelihood.

2. The method of claim 1 , wherein the immobilized microorganism is subjected to a condition.

3. The method of claim 2 , wherein the condition is associated with the event.

4. The method of claim 2 , wherein the condition is at least one of a temperature, a growth medium condition, a carbon source, a nitrogen source, an amino acid, a nutrient, a salt, a metal ion, a cofactor, a pH, a trace element, a dissolved gas, an antimicrobial agent, an aerobic condition, and an anaerobic condition.

5. The method of claim 2 , wherein the condition is at least one of static and dynamic.

6. The method of claim 1 , wherein the control growth rate is at least one of a predetermined growth rate and a dynamically determined growth rate.

7. The method of claim 1 , wherein the event is at least one of a predetermined time, a dynamically determined mass, a number of individuated microorganisms, and a number of clones.

8. The method of claim 1 , wherein the attribute is a plurality of attributes is evaluated simultaneously.

9. The method of claim 1 , further comprising determining, by the computer-based system, microorganism susceptibility based on a comparison of the growth likelihood value to a range.

10. The method of claim 1 , wherein the first wavelet transformation further comprises rendering, by the computer-based system, the first set of at least one of signals that are greater than the predetermined threshold and associated with the immobilized microorganism into a plurality of signal approximations.

11. The method of claim 10 , wherein the first wavelet transformation further comprises wherein the plurality of signal approximations are planes comprising a plurality of point amplitudes corresponding to microorganism locations.

12. The method of claim 11 , wherein the first wavelet transformation further comprises combining, by the computer-based system, the signal approximations to create a microorganism model.

13. The method of claim 11 , wherein the first wavelet transformation further comprises analyzing, by the computer-based system, the point amplitudes associated with at least one of background information and noise information.

14. The method of claim 13 , wherein the first wavelet transformation further comprises filtering, by the computer-based system, the signal approximations to eliminate the at least one of background information and noise information.

15. The method of claim 14 , wherein the first wavelet transformation further comprises registering, by the computer-based system, locations associated with point amplitudes corresponding to the immobilized microorganism.

16. The method of claim 1 , wherein the immobilized microorganism is an individuated microorganism.