Dataset Information

A new method of mark detection for software-based optical mark recognition.

ABSTRACT: Software optical mark recognition (SOMR) is the process whereby information entered on a survey form or questionnaire is converted using specialized software into a machine-readable format. SOMR normally requires input fields to be completely darkened, have no internal labels, or be filled with a soft pencil, otherwise mark detection will be inaccurate. Forms can also have print and scan artefacts that further increase the error rate. This article presents a new method of mark detection that improves over existing techniques based on pixel counting and simple thresholding. Its main advantage is that it can be used under a variety of conditions and yet maintain a high level of accuracy that is sufficient for scientific applications. Field testing shows no software misclassification in 5695 samples filled by trained personnel, and only two misclassifications in 6000 samples filled by untrained respondents. Sensitivity, specificity, and accuracy were 99.73%, 99.98%, and 99.94% respectively, even in the presence of print and scan artefacts, which was superior to other methods tested. A separate direct comparison for mark detection showed a sensitivity, specificity, and accuracy respectively of 99.7%, 100.0%, 100.0% (new method), 96.3%, 96.0%, 96.1% (pixel counting), and 99.9%, 99.8%, 99.8% (simple thresholding) on clean forms, and 100.0%, 99.1%, 99.3% (new method), 98.4%, 95.6%, 96.2% (pixel counting), 100.0%, 38.3%, 51.4% (simple thresholding) on forms with print artefacts. This method is designed for bubble and box fields, while other types such as handwriting fields require separate error control measures.

SUBMITTER: Loke SC

PROVIDER: S-EPMC6226159 | biostudies-other | 2018

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A new method of mark detection for software-based optical mark recognition.

Loke Seng Cheong SC Kasmiran Khairul A KA Haron Sharifah A SA

PloS one 20181109 11

Software optical mark recognition (SOMR) is the process whereby information entered on a survey form or questionnaire is converted using specialized software into a machine-readable format. SOMR normally requires input fields to be completely darkened, have no internal labels, or be filled with a soft pencil, otherwise mark detection will be inaccurate. Forms can also have print and scan artefacts that further increase the error rate. This article presents a new method of mark detection that imp ...[more]

PMID: 30412588

Similar Datasets

Project description:BackgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably-across different experimental data sets-without significantly compromising the accuracy of manual analysis.MethodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy 'F1' score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.ResultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1-6%, yielding high F1 scores of 0.96-0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.ConclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Project description:Background & aimsCurrently, staging of fibrosis in preclinical rodent liver fibrosis models is achieved histologically. Many animals are used at multiple time-points to assess disease progression or therapeutic responses. Hepatic myofibroblasts promote liver fibrosis therefore quantifying these cells in vivo could assess disease or predict therapeutic responses in mice. We fluorescently labelled a single chain antibody (C1-3) that binds hepatic myofibroblasts to monitor fibrogenesis in vivo.MethodsCCl4 was used to induce acute liver injury in WT and cRel(-/-) mice. Bile duct ligation was used to model chronic fibrosis. Hepatic myofibroblasts were depleted using a liposome-drug delivery system or chemically with sulfasalazine. An IVIS® spectrum visualised fluorophore-conjugated C1-3 in vivo.ResultsIVIS detection of fluorescently labelled-C1-3 but not a control antibody discriminates between fibrotic and non-fibrotic liver in acute and chronic liver fibrosis models. cRel(-/-) mice have a fibro-protective phenotype and IVIS signal is reduced in CCl4 injured cRel(-/-) mice compared to wild-type. In vivo imaging of fluorescently labelled-C1-3 successfully predicts reductions in hepatic myofibroblast numbers in fibrotic liver disease in response to therapy.ConclusionsWe report a novel fluorescence imaging method to assess murine hepatic myofibroblast numbers in vivo during liver fibrosis and after therapy. We also describe a novel liposomal antibody targeting system to selectively deliver drugs to hepatic myofibroblasts in vivo. C1-3 binds human hepatic myofibroblast therefore imaging labelled-C1-3 could be used for clinical studies in man to help stage fibrosis, demonstrate efficacy of drugs that promote hepatic myofibroblast clearance or predict early therapeutic responses.Lay summaryIn response to damage and injury scars develop in the liver and the main cell that makes the scar tissue is the hepatic myofibroblast (HM). C1-3 is an antibody fragment that binds to the scar forming HM. We have fluorescently labelled C1-3 and given it to mice that have either normal or scarred livers (which contain HM) and then used a machine called an in vivo imaging system (IVIS) that takes pictures of different wavelengths of light, to visualise the antibody binding to HM inside the living mouse. Using fluorescently labelled C1-3 we can assess HM numbers in the injured liver and monitor response to therapy. We have also used C1-3 to target drugs encapsulated in lipid carriers (liposomes) to the HM to kill the HM and reduce the liver disease.

Dataset Information

A new method of mark detection for software-based optical mark recognition.

Publications

A new method of mark detection for software-based optical mark recognition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets