1:45 PM - 3:15 PM
[O08-P74] Radiation Imaging using a Web Camera and image analysis
Keywords:Radiation Imaging , Web Camera , Image analysis, Electron beam
Radiation Imaging using a Web Camera and image analysis
Presenter Aito Uchida
Makuhari High School (Shibumaku)
1. the purpose
Detecting cosmic rays, radiation particles, and electromagnetic waves [1,5] with a web camera is possible based on the principles of image sensors.if visible light is blocked so that only radiation and cosmic rays reach the image sensor. Theoretically, measurements can be made by modifying the camera.
I used an easily available web camera to measure radiation over long exposures, analyzed the images obtained for each radiation source(152Eu, 241Am, 90Sr), and identified alpha, beta, and gamma rays. As a result of the study, it was found that there were significant differences in the shape and brightness of the trajectory depending on the radiation source. Therefore, we compared the knowledge of radiation [2,3,4,5,7] and the characteristics of radiation observed in a cloud chamber [6] with the characteristics of the trajectory that appeared in images measured by a web camera.
So I hypothesized that identification would be possible, and that quantitative identification would be possible by quantifying the length and area of the trajectory, and I performed image analysis based on this hypothesis.
I also verified the effect of the incident angle of radiation on the sensor on the image analysis results using electron beam measurements.
2. experimental method
Remove the lens from the web camera. During exposure, the radiation source is placed on top of the image sensor, and visible light is blocked with aluminum foil.
The camera is connected to a computer and long exposures are performed using software [4]. The obtained measurement images will be analyzed using code using openCV and Python. The radiation sources used 152Eu, 241Am, and 90Sr.
3. result
So far, I have reported that it is possible to distinguish between beta-rays and gamma-rays using image analysis method1.
In the analysis results using image analysis method2 an elongated trajectory that appears to be the beta-ray of 90Sr showed low brightness. Round trajectories also showed a distribution of average brightness similar to elongated trajectories, but I thought this was due to the angle at which the radiation enters the image sensor, causing it to be detected as round. This is because in the case of this measurement, a radiation source was used, so all the trajectories detected by long-time exposure of 90Sr are considered to be beta-rays. The elongated trajectories of 152Eu and 241Am showed a distribution similar to beta-rays of Sr. The round trajectory showed higher average brightness than the elongated trajectory. As for 241Am, the distribution of round trajectories was assumed to be gamma-rays. As a result of more detailed analysis (Table1), I obtained results that support the considerations of analysis method2. On the graph of 90Sr, the peak near the aspect ratio of 0 indicates beta-rays with an elongated trajectory, and the peak near the aspect ratio of 0.2 to 0.45 indicates beta-rays with a slightly round shape due to the influence of the angle of incidence on the sensor. 152Eu and 241Am behave similarly and exhibit beta-rays. In addition, the peaks in the vicinity of the aspect ratio of 0.6 to 0.8 in each graph indicate gamma-rays with a shape close to a circle. These were consistent with the characteristics of each radiation in the literature [2,6,7]. In other words, it is possible to identify beta-rays and gamma-rays by numerically analyzing the length, area, and average brightness of the trajectory that appears in the image, and it is also possible to distinguish between beta-rays and gamma-rays by quantifying the shape of the trajectory using the aspect ratio of the trajectory. I was able to express the characteristics of radiation: beta-rays have long, thin trajectories, and gamma-rays have small, round trajectories. As a result, it can be said that I succeeded in quantitatively identifying each radiation source through image analysis that digitized the characteristics of the trajectory left by radiation through measurements using a web camera.
4. Consideration and verification - Observation of electron beam using a web camera
The influence of the angle of incidence of radiation on the image sensor on the image analysis results, which had been a problem during the research process, was verified through measurements of KEK's electron beam.
Presenter Aito Uchida
Makuhari High School (Shibumaku)
1. the purpose
Detecting cosmic rays, radiation particles, and electromagnetic waves [1,5] with a web camera is possible based on the principles of image sensors.if visible light is blocked so that only radiation and cosmic rays reach the image sensor. Theoretically, measurements can be made by modifying the camera.
I used an easily available web camera to measure radiation over long exposures, analyzed the images obtained for each radiation source(152Eu, 241Am, 90Sr), and identified alpha, beta, and gamma rays. As a result of the study, it was found that there were significant differences in the shape and brightness of the trajectory depending on the radiation source. Therefore, we compared the knowledge of radiation [2,3,4,5,7] and the characteristics of radiation observed in a cloud chamber [6] with the characteristics of the trajectory that appeared in images measured by a web camera.
So I hypothesized that identification would be possible, and that quantitative identification would be possible by quantifying the length and area of the trajectory, and I performed image analysis based on this hypothesis.
I also verified the effect of the incident angle of radiation on the sensor on the image analysis results using electron beam measurements.
2. experimental method
Remove the lens from the web camera. During exposure, the radiation source is placed on top of the image sensor, and visible light is blocked with aluminum foil.
The camera is connected to a computer and long exposures are performed using software [4]. The obtained measurement images will be analyzed using code using openCV and Python. The radiation sources used 152Eu, 241Am, and 90Sr.
3. result
So far, I have reported that it is possible to distinguish between beta-rays and gamma-rays using image analysis method1.
In the analysis results using image analysis method2 an elongated trajectory that appears to be the beta-ray of 90Sr showed low brightness. Round trajectories also showed a distribution of average brightness similar to elongated trajectories, but I thought this was due to the angle at which the radiation enters the image sensor, causing it to be detected as round. This is because in the case of this measurement, a radiation source was used, so all the trajectories detected by long-time exposure of 90Sr are considered to be beta-rays. The elongated trajectories of 152Eu and 241Am showed a distribution similar to beta-rays of Sr. The round trajectory showed higher average brightness than the elongated trajectory. As for 241Am, the distribution of round trajectories was assumed to be gamma-rays. As a result of more detailed analysis (Table1), I obtained results that support the considerations of analysis method2. On the graph of 90Sr, the peak near the aspect ratio of 0 indicates beta-rays with an elongated trajectory, and the peak near the aspect ratio of 0.2 to 0.45 indicates beta-rays with a slightly round shape due to the influence of the angle of incidence on the sensor. 152Eu and 241Am behave similarly and exhibit beta-rays. In addition, the peaks in the vicinity of the aspect ratio of 0.6 to 0.8 in each graph indicate gamma-rays with a shape close to a circle. These were consistent with the characteristics of each radiation in the literature [2,6,7]. In other words, it is possible to identify beta-rays and gamma-rays by numerically analyzing the length, area, and average brightness of the trajectory that appears in the image, and it is also possible to distinguish between beta-rays and gamma-rays by quantifying the shape of the trajectory using the aspect ratio of the trajectory. I was able to express the characteristics of radiation: beta-rays have long, thin trajectories, and gamma-rays have small, round trajectories. As a result, it can be said that I succeeded in quantitatively identifying each radiation source through image analysis that digitized the characteristics of the trajectory left by radiation through measurements using a web camera.
4. Consideration and verification - Observation of electron beam using a web camera
The influence of the angle of incidence of radiation on the image sensor on the image analysis results, which had been a problem during the research process, was verified through measurements of KEK's electron beam.