An image processing application for quantification of protein aggregates in Caenorhabditis elegans

Protein aggregation became a widely accepted marker of many polyQ disorders, including Machado-Joseph disease (MJD), and is often used as readout for disease progression and development of therapeutic strategies. The lack of good platforms to rapidly quantify protein aggregates in a wide range of disease animal models prompted us to generate a novel image processing application that automatically identifies and quantifies the aggregates in a standardized and operator-independent manner. We propose here a novel image processing tool to quantify the protein aggregates in a Caenorhabditis elegans (C. elegans) model of MJD. Confocal mi-croscopy images were obtained from animals of different genetic conditions. The image processing application was developed using MeVisLab as a platform to pro-cess, analyse and visualize the images obtained from those animals. All segmenta-tion algorithms were based on intensity pixel levels.The quantification of area or numbers of aggregates per total body area, as well as the number of aggregates per animal were shown to be reliable and reproducible measures of protein aggrega-tion in C. elegans. The results obtained were consistent with the levels of aggrega-tion observed in the images. In conclusion, this novel imaging processing applica-tion allows the non-biased, reliable and high throughput quantification of protein aggregates in a C. elegans model of MJD, which may contribute to a significant improvement on the prognosis of treatment effectiveness for this group of disor-ders

Application to quantify fetal lung branching on rat explants

Recently, regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. The development of accurate and reliable segmentation techniques may be essential to improve research outcomes. This work presents an image processing method to measure the perimeter and area of lung branches on fetal rat explants. The algorithm starts by reducing the noise corrupting the image with a pre-processing stage. The outcome is input to a watershed operation that automatically segments the image into primitive regions. Then, an image pixel is selected within the lung explant epithelial, allowing a region growing between neighbouring watershed regions. This growing process is controlled by a statistical distribution of each region. When compared with manual segmentation, the results show the same tendency for lung development. High similarities were harder to obtain in the last two days of culture, due to the increased number of peripheral airway buds and complexity of lung architecture. However, using semiautomatic measurements, the standard deviation was lower and the results between independent researchers were more coherent

Qualifound – a modular tool developed for quality improvement in foundries

Purpose – Castings defects are usually easy to characterize, but to eradicate them can be a difficult task. In many cases, defects are caused by the combined effect of different factors, whose identification is often difficult. Besides, the real non-quality costs are usually unknown, and even neglected. This paper aims to describe the development of a modular tool for quality improvement in foundries, and its main objective is to present the application potential and the foundry process areas that are covered and taken into account. Design/methodology/approach – The integrated model was conceived as an expert system, designated Qualifound, which performs both qualitative and quantitative analyses. For the qualitative analyses mode, the nomenclature and the description of defects are based on the classification suggested by the International Committee of the Foundry Technical Association. Thus, a database of defects was established, enabling one to associate the defects with the relevant process operations and the identification of their possible causes. The quantitative analysis mode deals with the number of produced and rejected castings and includes the calculation of the non-quality costs. Findings – The validation of Qualifound was carried out in a Portuguese foundry, whose quality system had been certified according to the ISO 9000 standards. Qualifound was used in every management area and it was concluded that the application had the required technological requisites to provide the necessary information for the foundry management to improve process quality. Originality/value – The paper presents a successful application of an informatics tool on quality improvement in foundries.

The importance of quality in the consolidation of textile in Minho

The textile industry has a long tradition in Portugal and it is one of the most important sectors, despite the current economic crisis. It has always assumed a prominent role in terms of employment and a relevant position within the Portuguese economy. The lack of quality and the lower prices that other countries offer causes the loss of clients. Quality is a main tool to survive nowadays in the textile sector. To undertake our analysis, we made use of an existing database where 55 firms belonged to the textile industry, namely to the manufacturing sector. A new survey was created based on the original survey and was sent to 5 firms. Besides the survey, we also sent a few questions to the firms in order to retract more information about the actually situation in our country, concerning the textile industry. Several tables, graphs and pie charts were made to help shed light on our findings. This research was conducted in order to determine the importance of quality in the consolidation of textile firms in the north of Portugal. Most firms in our sample feel that quality improvement, business benefits, mobilizing employees’ knowledge and business image were important and that competition is very intense and is mainly by price and not by differentiation of product or service. The quality program has contributed to improve their competitive position and the improvement of their overall performance. The majority of the firms in our sample undertake TQM measures for quality purposes to meet customer expectations and prevent errors. Of all firms surveyed, the quality is certainly very important for its survival.

Application to quantify fetal lung branching on rat explant

Recently, regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. The development of accurate and reliable segmentation techniques may be essential to improve research outcomes. This work presents an image processing method to measure the perimeter and area of lung branches on fetal rat explants. The algorithm starts by reducing the noise corrupting the image with a pre-processing stage. The outcome is input to a watershed operation that automatically segments the image into primitive regions. Then, an image pixel is selected within the lung explant epithelial, allowing a region growing between neighbouring watershed regions. This growing process is controlled by a statistical distribution of each region. When compared with manual segmentation, the results show the same tendency for lung development. High similarities were harder to obtain in the last two days of culture, due to the increased number of peripheral airway buds and complexity of lung architecture. However, using semiautomatic measurements, the standard deviation was lower and the results between independent researchers were more coherent.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.