Data Extraction in Insurance Photo-Inspections Using Computer Vision

[Abstract] Recent advances in computer vision and artificial intelligence allow for a better processing of complex information in many fields of human activity. One such field is vehicle expertise and inspection. This paper presents the development of systems for the automatic reading of French and Spanish license plates, as well as odometer value reading in dashboard photographs. These were trained and validated with real examples of more than 4000 vehicles, while addressing typical problems with irregular data acquisition. The systems proposed have found use in a real environment and are employed as assistance in vehicle appraisal.This research was co-funded by Ministerio de Ciencia, Innovación y Universidades and the European Union (European Regional Development Fund—ERDF) as part of the RTC-2017-5908-7 research project. We wish to acknowledge the support received from the Centro de Investigación de Galicia CITIC, funded by Consellería de Educación, Universidade e Formación Profesional from Xunta de Galicia and European Union. (European Regional Development Fund - FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01Xunta de Galicia; ED431G 2019/0

Enhancing Pathological Detection and Monitoring in OCT Volumes with Limited Slices using Convolutional Neural Networks and 3D Visualization Techniques

Cursos e Congresos, C-155[Abstract] Optical Coherence Tomography (OCT) is a non-invasive imaging technique with a crucial role in the monitoring of a wide range of diseases. In order to make a good diagnosis it is essential that clinicians can observe any subtle changes that appear in the multiple ocular structures, so it is imperative that the 3D OCT volumes have good resolution in each axis. Unfortunately, there is a trade-off between image quality and the number of volume slices. In this work, we use a convolutional neural network to generate the intermediate synthetic slices of the OTC volumes and we propose a few variants of a 3D reconstruction algorithm to create visualizations that emphasize the changes present in multiple retinal structures to aid clinicians in the diagnostic processXunta de Galicia; ED431C 2020/24This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, PDC2022-133132-I00 and TED2021-131201B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by ”Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by ”Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project

Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies

[Abstract] In this work we propose the automatic estimation of the visual acuity of patients with retinal vein occlusion using Optical Coherence Tomography by Angiography (OCTA) images. To do this, we first extract the most relevant biomarkers in this imaging modality—area of the foveal avascular zone and vascular densities in different regions of the OCTA image. Then, we use a support vector machine to estimate the visual acuity. We obtained a mean absolute error of 0.1713 between the manual visual acuity measurement and the estimated, being considered satisfactory results.Centro de Investigación de Galicia; ED431G 2019/01Xunta de Galicia; DTS18/00136Ministerio de Ciencia, Innovación y Universidades; RTI2018-095894-B-I0

Wavelet-based methodology for [15O]-H20 PET brain activation assessment

[Abstract] AMI International Conference 2003, September 21-27, Madrid, Spain: "High Resolution Molecular Imaging: from Basic Science to Clinical Applications"Statistical parametric mapping (SPM) is a voxel-byvoxel analysis method commonly used for the detection of brain activation patterns. An alternative approach is the use of multiscale information by means of wavelet analysis. In this study, we have compared the detection of brain activations using conventional SPM and a statistical wavelet analysis in a set of realistic simulated [15O]-H20 positron emission tomography (PET) phantomsPublicad

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

[Abstract] Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.This work is supported by the European Regional Development Fund (ERDF) of the European Union and Xunta de Galicia through Centro de Investigación del Sistema Universitario de Galicia, ref. ED431G 2019/01Xunta de Galicia; ED431G 2019/0

Tracking Bacterial Spoilage in Cosmetics by a New Bioanalytical Approach: API-SPME-GC-MS to Monitor MVOCs

The main goal of this work was the use of the powerful solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) technique to unequivocally identify microbial volatile organic compounds (MVOCs) derived from the enzymatic activity produced during metabolic processes using analytical profile index (API) biochemical tests. Three bacteria were selected for this study: Escherichia coli, Proteus mirabilis, and Pseudomonas aeruginosa. They were inoculated and incubated to both API components and real cosmetics, as well as to a mixture of them. Specific MVOCs were successfully identified as biomarkers for each one of the studied microorganisms: Indole and 2-nitrophenol as Escherichia coli markers, 2-undecanone and phenylethyl alcohol as Proteus mirabilis-specific markers, and 1-undecene and 2′-aminoacetophenone as Pseudomonas aeruginosa ones. In addition, a high number of MVOCs were identified as general markers of bacterial presence. The results revealed that the MVOCs’ formation is highly subtract dependent. Therefore, the ultimate and most challenging objective is to establish a relationship between the identified MVOCs and the original compound present in the substrate. This work establishes the design and development of this original approach, and its practical application to the control of microbial contamination in real cosmetic samplesThis research was funded by project QFashion (IN852A 2016/157, CONECTA-PEME, Xunta de Galicia). The authors belong to the CRETUS Strategic Partnership (ED431E 2018/01). All these programmes are co-funded by FEDER (UE)S

Automatic Pipeline for Detection and Classification of Phytoplankton Specimens in Digital Microscopy Images of Freshwater Samples

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Phytoplankton blooming can compromise the quality of the water and its safety due to the negative effects of the toxins that some species produce. Therefore, the continuous monitoring of water sources is typically required. This task is commonly and routinely performed by specialists manually, which represents a major limitation in the quality and quantity of these studies. We present an accurate methodology to automate this task using multi-specimen images of phytoplankton which are acquired by regular microscopes. The presented fully automatic pipeline is capable of detecting and segmenting individual specimens using classic computer vision algorithms. Furthermore, the method can fuse sparse specimens and colonies when needed. Moreover, the system can differentiate genuine phytoplankton from other similar non-phytoplanktonic objects like zooplankton and detritus. These genuine phytoplankton specimens can also be classified in a target set of species, with special focus on the toxin-producing ones. The experiments demonstrate satisfactory and accurate results in each one of the different steps that compose this pipeline. Thus, this fully automatic system can aid the specialists in the routine analysis of water sources.This research was funded by Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/147 and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).Xunta de Galicia; ED481A 2021/147Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

Fully automatic detection and classification of phytoplankton specimens in digital microscopy images

©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article Rivas-Villar, D., Rouco, J., Carballeira, R., Penedo, M. G., & Novo, J. (2021). “Fully automatic detection and classification of phytoplankton specimens in digital microscopy images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 200(105923), 105923. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2020.105923.[Abstract]: Background and objective: The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process. Methods: This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach. Results: The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the ”Other” group, a set of relevant toxic and interesting species widely spread over the samples. Conclusions: The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.This work is supported by the European Regional Development Fund (ERDF) of the European Union and Xunta de Galicia through Centro de Investigación del Sistema Universitario de Galicia, ref. ED431G 2019/01.Xunta de Galicia; ED431G 2019/0

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007.[Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.This work was partially supported by the Research Project RTC-2016-5143-1, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). Also, this work has received financial support from the ERDF and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04