An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual

Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and the data are not captured for water service providers. Here we present an automated toolchain designed to process digital images of free chlorine residual test strips taken with mobile phone cameras. The toolchain crops the image using image processing algorithms that isolate the areas relevant for analysis and automatically white balances the image to allow for use with different phones and lighting conditions. The average red, green, and blue (RGB) color values of the image are used to predict a free chlorine concentration that is classified into three concentration tiers (<0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L), which can be reported to water users and recorded for utility use. The proposed approach was applied to three different phone types under three different lighting conditions using a standard background. This approach can discriminate between concentrations above and below 0.5 mg/L with an accuracy of 90% and 94% for training and testing data sets, respectively. Furthermore, it can discriminate between concentrations of <0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L with weighted-averaged F1 scores of 79% and 88% for training and testing data sets, respectively. This tool sets the stage for tap water consumers and water utilities to gather frequent measurements and high-resolution temporal and spatial data on drinking water quality.

A Technological System for Post-Earthquake Damage Scenarios Based on the Monitoring by Means of an Urban Seismic Network

A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision-makers in planning the first post-disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and supporting rescue and recovery. This system can be even more useful when it works on densely populated areas, as well as on historic urban centers. In the paper, we propose a processing chain on a GIS platform to generate post-earthquake damage scenarios, which are based: (1) on the near real-time processing of the ground motion, that is recorded in different sites by MEMS accelerometric sensor network in order to take into account the local effects, and (2) the current structural characteristics of the built heritage, that can be managed through an information system from the local public administration authority. In the framework of the EU-funded H2020-ARCH project, the components of the system have been developed for the historic area of Camerino (Italy). Currently, some experimental fragility curves in the scientific literature, which are based on the damage observations after Italian earthquakes, are implemented in the platform. These curves allow relating the acceleration peaks obtained by the recordings of the ground motion with the probability to reach a certain damage level, depending on the structural typology. An operational test of the system was performed with reference to an ML3.3 earthquake that occurred 13 km south of Camerino. Acceleration peaks between 1.3 and 4.5 cm/s² were recorded by the network, and probabilities lower than 35% for negligible damage (and then about 10% for moderate damage) were calculated for the historical buildings given this low-energy earthquake.

gQuant, an Automated Tool for Quantitative Glycomic Data Analysis

MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.

An automated and multiparametric algorithm for objective analysis of meibography images

Background

Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed.

Methods

An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (r_P ) and the false-negative rate (r_N ) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images.

Results

The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94±0.02, the false-positive rate (r_P )=6.02%±2.41%, and the false-negative rate (r_N )=6.43%±1.98%; for meibomian gland segmentation, the similarity index (k)=0.87±0.01, the false-positive rate (r_P )=4.35%±1.50%, and the-false negative rate (r_N )=18.61%±1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI).

Conclusions

A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

PURPOSE

To describe, validate, and implement a centerline processing scheme (CPS) for semiautomated segmentation and quantification in carotid siphons of healthy subjects. 4D flow MRI enables blood flow measurement in all major cerebral arteries with one scan. Clinical translational hurdles are time demanding postprocessing and user-dependence induced variability during analysis.

MATERIALS AND METHODS

A CPS for 4D flow data was developed to automatically separate cerebral artery trees. Flow parameters were quantified at planes along the centerline oriented perpendicular to the vessel path. At 3T, validation against 2D phase-contrast (PC) magnetic resonance imaging (MRI) and 4D flow manual processing was performed on an intracranial flow phantom for constant flow, while pulsatile flow validation was performed in the internal carotid artery (ICA) of 10 healthy volunteers. The CPS and 4D manual processing times were measured and compared. Flow and area measurements were also demonstrated along the length of the ICA siphon.

RESULTS

Phantom measurements for area and flow were highly correlated between the CPS and 2D measurements (area: R = 0.95, flow: R = 0.94), while in vivo waveforms were highly correlated (R = 0.93). Processing time was reduced by a factor of 4.6 compared with manual processing. Whole ICA measurements revealed a significantly decreased area in the most distal segment of the carotid siphon (P = 0.0017), with flow unchanged (P = 0.84).

CONCLUSION

This study exhibits fast semiautomated analysis of intracranial 4D flow MRI. Internal consistency was shown through flow conservation along the tortuous ICA siphon, which is typically difficult to assess.