Renovating Glucometers

A great chunk of today's biological research focuses on improving existing technologies. For example, using image processing for cell counting in fluorescent stained leaf stem, or  finding techniques for efficient computational methods for studying molecular, structural or cellular biology. Biologists today are also going the smart way by using smartphones as a computing platform. EyeNetra (Link: www.eyenetra.com) , a handheld device that integrates with a smartphone and provides vision correction technology to the masses is a great example.

A similar research is being conducted to replace the tradition glucometers with handheld devices wherein simple image capture of the blood impregnated strip will deduce the glucose content in blood.

Microfluidic paper when saturated with blood yield varied intensities of color where the intensity of the color developed is inversely proportional to the glucose content in blood. Most of us will be familiar with Accu-Chek strips that are used by diabetic patients to test their insulin content. In the particular case of Accu-Chek strips, a greenish color develops when saturated with human blood. The luminosity of the color developed is higher for lesser glucose content (Simply said, higher the glucose concentration, darker is the color developed).

Such is the plot obtained for glucose concentration vs. the color developed on the Accu-Chek strips:

We found that the glucose concentration correlated with the luminosity of color developed better as compared to various other combinations of the red, green or blue components.

We imagined an app that could capture the image of the strip and find the luminosity of the circular region on the strip. A person could thus find his/her blood glucose level without the use of traditional glucometers. This smartphone based app can also suggests remedies and digitally transmit the result along with the phone‘s location to a central database. This can be used to estimate demographics consisting of people with abnormal glucose levels.

A technique was required for segmenting the circular disk in the Accu-Chek strips. We used snakuscules for their ability to segment circular contours. Following is the sequence in which a snakuscule captures circular disk on the strip.

A patient capturing the strip's image may be present in different environment settings and under varied ambient lighting conditions. Although the color produced on the strip will be similar for similar glucose levels, but it may be perceived as different due to presence of external ambient lighting. It was thus necessary to normalize the colors using a color constancy algorithm. 

Von-Kries coffecient Law for color constancy gave the following results, where 2 different strips with same glucose content, but in different ambient lighting were converted into images with similar colors.

a)                                                      b)                                               c)                                                d)

RGB Correction: Von-Kries Coefficient Law can also be used to add illumination to dark images. Figure (b) and (d) are obtained from figure (a) and (c) respectively.

After normalizing the illumination and segmenting the disk, we calculated the luminosity of the color developed by averaging the luminosity of all individual pixels inside the disk. A final equation was plotted for glucose concentration vs. luminosity (Complete data not shown).

The above strategy can be used for finding the glucose concentration of any strip using the information about the color developed upon blood impregnation. A smartphone app developed using such an algorithm can greatly benefit third world countries where in low cost, portable devices can benefit the masses. With more than 347 million people suffering from diabetes worldwide, such technology can be efficiently used for getting quick and reliable results. 

Snakuscules

Recently got familiar with a methodology for contour segmentation used in biomedical image processing. Researchers at the 'Biomedical Imaging Group' of École polytechnique fédérale de Lausanne (EPFL), worked on segmenting approximate circular regions in images using the concepts of snakes, known as Snakuscule.

A snakuscule is a simple active counter that preys upon bright blobs in an image. Here is my implementation:

Snakuscule enveloping a bright blob in an image

Such active contours move under the influence of energy gradients. For every snakuscule, the energy difference between the outer adjoining annulus and the inner disk is calculated. Such active contours can be programmed to prey upon bright blobs in an image. Hence, they move in the direction of decreasing energy difference.

Energy to be minimized can be given by the equation:

To minimize energy, and to detect brighter blobs, the snakuscule can move in any of the four directions, or vary its radius. Out of these six possible actions, snakuscule selects the one that maximizes the decrease in energy.

We normalize the energy function by dividing the energy of outer annulus and inner disk by the area constituting them respectively.

Normalized form:

A more rigorous implementation can be seen in the original paper on snakuscules by Philippe Thévenaz and Michael Unser. 

Matlab codes for the same can be found at: github.com/sanyamgarg93/Snakuscules