Automatic colorimetric calibration of human wounds

Contains fulltext : 88431.pdf (publisher's version ) (Open Access)BACKGROUND: Recently, digital photography in medicine is considered an acceptable tool in many clinical domains, e.g. wound care. Although ever higher resolutions are available, reproducibility is still poor and visual comparison of images remains difficult. This is even more the case for measurements performed on such images (colour, area, etc.). This problem is often neglected and images are freely compared and exchanged without further thought. METHODS: The first experiment checked whether camera settings or lighting conditions could negatively affect the quality of colorimetric calibration. Digital images plus a calibration chart were exposed to a variety of conditions. Precision and accuracy of colours after calibration were quantitatively assessed with a probability distribution for perceptual colour differences (dE_ab). The second experiment was designed to assess the impact of the automatic calibration procedure (i.e. chart detection) on real-world measurements. 40 Different images of real wounds were acquired and a region of interest was selected in each image. 3 Rotated versions of each image were automatically calibrated and colour differences were calculated. RESULTS: 1st Experiment: Colour differences between the measurements and real spectrophotometric measurements reveal median dE_ab values respectively 6.40 for the proper patches of calibrated normal images and 17.75 for uncalibrated images demonstrating an important improvement in accuracy after calibration. The reproducibility, visualized by the probability distribution of the dE_ab errors between 2 measurements of the patches of the images has a median of 3.43 dE* for all calibrated images, 23.26 dE_ab for all uncalibrated images. If we restrict ourselves to the proper patches of normal calibrated images the median is only 2.58 dE_ab! Wilcoxon sum-rank testing (p < 0.05) between uncalibrated normal images and calibrated normal images with proper squares were equal to 0 demonstrating a highly significant improvement of reproducibility. In the second experiment, the reproducibility of the chart detection during automatic calibration is presented using a probability distribution of dE_ab errors between 2 measurements of the same ROI. CONCLUSION: The investigators proposed an automatic colour calibration algorithm that ensures reproducible colour content of digital images. Evidence was provided that images taken with commercially available digital cameras can be calibrated independently of any camera settings and illumination features

Optimizing the Visibility of Agricultural activities on the Farm using a sound analytics platform

Norway's agricultural sector is significant culturally and economically, and it thrives despite severe challenges. The production of dairy products is at the top, followed by those of sheep and pigs, with salmon aquaculture also on the rise. Governmentally approved strict criteria for biodiversity, emissions reduction, and sustainability (including livestock welfare) are used to cultivate crops including grains, potatoes, and organic vegetables. Sound-based analytics are used by cutting-edge AI technologies for monitoring farm machinery. The method uses supervised classification to pick out relevant audio data from the noise and then uses one-class classification to identify unique content. Over 20 distinct farming activities may be identified using multi-class classification, with assistance from farm boundary detection. With the Urban Sound dataset, we can see that Random Forest achieves 90% accuracy in just 22.83 seconds, which is significantly faster than XGBoost's time of 326.69 seconds. XGBoost can identify both human voices and garbage in 0.81 seconds, but Random Forest takes 1.27 seconds to do it with just 85% accuracy. XGBoost can identify agricultural tasks with 97% accuracy in about 0.04 seconds, while Decision Trees can only get to 95% accuracy. These changes show how technology is improving life in Norway's rural areas