Subjective Visual Inspection Research Articles

Field Imaging and Volumetric Reconstruction of Riprap Rock and Large-Sized Aggregates: Algorithms and Application

Riprap rock and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering. They essentially provide erosion control, sediment control, and scour protection. The sustainable and reliable use of riprap materials demands efficient and accurate evaluation of their large particle sizes, shapes, and gradation information at both quarry production lines and construction sites. Traditional methods for assessing riprap geometric properties involve subjective visual inspection and time-consuming hand measurements. As such, achieving the comprehensive in-situ characterization of riprap materials still remains challenging for practitioners and engineers. This paper presents an innovative approach for characterizing the volumetric properties of riprap by establishing a field imaging system associated with newly developed color image segmentation and three-dimensional (3-D) reconstruction algorithms. The field imaging system described in this paper with its algorithms and field application examples is designed to be portable, deployable, and affordable for efficient image acquisition. The robustness and accuracy of the image segmentation and 3-D reconstruction algorithms are validated against ground truth measurements collected in stone quarry sites and compared with state-of-the-practice inspection methods. The imaging-based results show good agreement with the ground truth and provide improved volumetric estimation when compared with currently adopted inspection methods. Based on the findings of this study, the innovative imaging-based system is envisioned for full development to provide convenient, reliable, and sustainable solutions for the onsite Quality Assurance/Quality Control tasks relating to riprap rock and large-sized aggregates.

A Novel Method for the Objective Identification of Hyperautofluorescent Ring in Retinitis Pigmentosa Using Binarization Processing.

PurposeTo assess precision and accuracy of a new objective algorithm using binarization in a software for identifying the hyperautofluorescent ring (AF ring) in retinitis pigmentosa (RP) compared with subjective visual inspection.MethodsUltra-widefield AF images were obtained from 23 eyes of 13 patients with retinitis pigmentosa (RP). We defined the borders of the AF rings using semiautomatic binarization algorithm in Fiji software. We compared the degree of precision (intra- and interrater agreements) of this algorithm and that of subjective visual inspection (freehand method) using Jaccard indices (JIs). To compare the classification performance (whether 68 points of Humphrey Field Analyzer is classified as inside, on, or outside AF rings), we calculated percent agreement and weighted kappa statistic between the two methods. The relationship between the distance from the AF ring and retinal sensitivities was also investigated.ResultsThe binarization method showed significantly higher JIs than the freehand method (for interrater: 0.94–0.95 vs. 0.73–0.78, respectively, P = 0.002; for intrarater: 0.95 vs. 0.68–0.71, respectively, P = 0.005). Percent agreement for classification between the two methods were 0.94 and weighted kappa statistic was 0.94 (P < 0.001). The retinal sensitivities decreased significantly and eccentrically from 2° inside to 3° outside the AF ring.ConclusionsDefining the AF ring in RP using the binarization algorithm showed significantly higher precision and the same degree of accuracy compared with visual inspection.Translation RelevanceThis novel method may enable quantitative analysis of the AF ring, an indicator of retinal function in RP.

Feature-oriented singular value shrinkage for optical coherence tomography image

Optical coherence tomography (OCT) is a non-invasive optical imaging modality that has been widely used in the field of medical diagnosis. However, OCT images are often degraded by speckle noise. To address this problem, this paper proposes a two-stage feature-oriented singular value shrinkage algorithm in a low-rank approximation framework, for speckle noise reduction and contrast enhancement of intra-retinal layers of OCT images. First, a weighted absolute distance is employed to find nonlocal similar patches that exhibit high correlation to a given reference one. Next, the singular values of the group matrix formed by similar patches are shrunk by mixed thresholding so that they are closer to the singular values of that of latent noise-free image. Finally, an iterative regularization technique is adopted to improve the denoising performance of the proposed method, where the backward projection parameter in each pixel is adaptively determined by its corresponding gradient variation. By using these strategies, the proposed method not only effectively removes the speckle noise of OCT images, but also preserves fine structural information of objects. Experimental results show that the proposed algorithm is competitive with some state-of-the-art speckle removal techniques in terms of both objective metrics and subjective visual inspection.

A survey on automated melanoma detection

Skin cancer is defined as the rapid growth of skin cells due to DNA damage that cannot be repaired. Melanoma is one of the deadliest types of skin cancer, which originates from melanocytes. While other skin cancer types have limited spreading capabilities, the danger of melanoma comes from its ability to spread (metastasize) rapidly. Fortunately, it can be detected by visual inspection of the skin surface, and it is 100% curable if identified in the early stages. However, detection by “subjective visual inspection” creates an important problem, due to investigators’ different levels of experiences and education. Dermoscopy (dermatoscopy) has significantly increased the diagnostic accuracy of melanoma since late 90’s. In addition, several systems have been proposed in order to assist investigators or to perform an automatic melanoma detection. This survey focuses on the algorithms for automated melanoma detection in dermoscopic images through an extensive analysis of the stages in methodologies proposed in the literature, and by examining related concepts and describing possible future directions through open problems in this domain of research.

Clinical applicability of and changes in perfusion MR imaging in brain metastases after stereotactic radiotherapy

To assess the applicability of perfusion-weighted (PWI) magnetic resonance (MR) imaging in clinical practice, as well as to evaluate the changes in PWI in brain metastases before and after stereotactic radiotherapy (SRT), and to correlate these changes to tumor status on conventional MR imaging. Serial MR images at baseline and at least 3 and 6 months after SRT were retrospectively evaluated. Size of metastases and the relative cerebral blood volume (rCBV), assessed with subjective visual inspection in the contrast enhanced area, were evaluated at each time point. Tumor behavior of metastases was categorized into four groups based on predefined changes on MRI during follow-up, or on histologically confirmed diagnosis; progressive disease (PD), pseudoprogression (PsPD), non-progressive disease (non-PD) and progression unspecified (PU). Twenty-six patients with 42 metastases were included. Fifteen percent (26/168) of all PW images could not be evaluated due to localization near large vessels or the scalp, presence of hemorrhage artefacts, and in 31% (52/168) due to unmeasurable residual metastases. The most common pattern (52%, 13/25 metastases) showed a high rCBV at baseline and low rCBV during follow-up, occurring in metastases with non-PD (23%, 3/13), PsPD (38%, 5/13) and PU (38%, 5/13). Including only metastases with a definite outcome generally showed low rCBV in PsPD or non-PD, and high rCBV in PD. Although non-PD and PsPD may be distinguished from PD after SRT using the PW images, the large proportion of images that could not be assessed due to artefacts and size severely hampers value of PWI in predicting tumor response after SRT.

Development of a statistical method to help evaluating the transparency/opacity of decorative thin films

In this paper, we present a statistical method that allows evaluating the degree of a transparency of a thin film. To do so, the color coordinates are measured on different substrates, and the standard deviation is evaluated. In case of low values, the color depends on the film and not on the substrate, and intrinsic colors are obtained. In contrast, transparent films lead to high values of standard deviation, since the value of the color coordinates depends on the substrate. Between both extremes, colored films with a certain degree of transparency can be found.This method allows an objective and simple evaluation of the transparency of any film, improving the subjective visual inspection and avoiding the thickness problems related to optical spectroscopy evaluation. Zirconium oxynitride films deposited on three different substrates (Si, steel and glass) are used for testing the validity of this method, whose results have been validated with optical spectroscopy, and agree with the visual impression of the samples.

A novel objective method for assessing high-contrast spatial resolution in CT based on the Rayleigh criterion.

In China, most quality assessments (QAs) of the high-contrast resolution of medical computed tomography (CT) equipment is performed by subjective evaluation, which is simple but dependent on observer expertise. The purpose of this study was to compare the accuracy and degree of dispersion of evaluation results from observers with different expertise and to develop an objective, easily understandable new approach to minimize the error caused by the subjective nature of observer evaluation. The accuracy and reproducibility of this new approach was validated and compared using both subjective and objective evaluation methods. This new objective evaluation method of high-contrast resolution was based on measurements of CT phantom images. The intensity profile of the aluminum line pairs' (LPs') images was extracted and quantified following the Rayleigh criterion. The interpolation method was employed to improve the consistency of the middle profile lines of each LP group for application of the Rayleigh criterion. With the introduction of a normalized margin, the accuracy of the results increased to the decimal level. Five junior observers and three QA professionals evaluated the high-contrast resolution of three different CT scanners using the subjective method, modulation transfer function (MTF) method, and the new objective method, and the results were compared statistically. The experimental standard deviations ranged from 0.4LP/cm to 0.55LP/cm for subjective visual inspection. The uncertainty of the expert group's results was much lower than that of the volunteer group. The experimental standard deviations of the MTF evaluation, according to both the standard deviation (SD) and point spread function (PSF) methods, were within the intervals of (0.04-0.06)LP/cm and (0.08-0.16)LP/cm, whereas that of the proposed objective evaluation method was approximately 0.02LP/cm. The experimental data indicated that the results of the new objective evaluation were in accordance with those of the expert group's subjective evaluation and the MTF evaluation. In this study, the subjective evaluation results from experienced observers showed relatively less uncertainty and more accuracy than those from inexperienced observers. Furthermore, a new objective evaluation approach that is easily understood, accurate, and reproducible was developed in this study. Results acquired using this new method were comparable with those of expert groups' subjective evaluation and MTF evaluation. Specialized QA software that integrates the steps implemented in the new method is being developed to enhance the convenience of the approach and assist observers in obtaining accurate and replicable assessment results.

Evaluation of burn severity in vivo in a mouse model using spectroscopic optical coherence tomography.

Clinical management of burn injuries depends upon an accurate assessment of the depth of the wound. Current diagnostic methods rely primarily on subjective visual inspection, which can produce variable results. In this study, spectroscopic optical coherence tomography was used to objectively evaluate burn injuries in vivo in a mouse model. Significant spectral differences were observed and correlated with the depth of the injury as determined by histopathology. The relevance of these results to clinical burn management in human tissues is discussed.

Ripening of salami: Assessment of colour and aspect evolution using image analysis and multivariate image analysis

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection.

AN INNOVATIVE APPLICATION OF COMPUTERIZED CELL IMAGE ANALYSIS IN THE THERAPEUTIC AND CLINICAL FOLLOW UP — QUANTITATIVE EVALUATION OF THE CLINICOPATHOLOGICAL IMPROVEMENT IN A CASE OF CUTANEOUS MASTOCYTOSIS RECEIVING A SERIES OF UVA1 IRRADIATION

The severity and outcome of mastocytosis are known to be related to the number and size of mast cells. The standard treatment of this rare dermatological disorder is phototherapy with UVA1 irradiation. Here, the treatment effects were evaluated during the course of phototherapy on a patient with mastocytosis (urticaria pigmentosa). Photomicrographs of CD117 antibody-stained histological sections of the dermis were analyzed using image software programmed with artificial neural networks. The artificial neural networks analyzed the color feature of epidermis, dermis and mast cells. First, mast cells were isolated from epidermis and dermis, and RGB pixels were converted to HSV color space for artificial neural networks training. Secondly, morphology and connective component labeling were used in epidermal pixels classification to determine the scope of dermal areas. Thirdly, regional separation isolated the dermis for the mast cell analysis. Specifically, the number and size of mast cells were quantified. The method has good reproducibility compared with other methods which had larger variance and lower accuracy. We found also that the UVA1 phototherapy was effective as indicated by a post-treatment change in the ratio of mast cell surface to cell number. In addition, we compared similar results based on subjective visual inspection (from 10 human subjects). Both methods of analysis showed a reduction in the number and size of mast cells after phototherapy. But compared with human judgment, the computerized method was superior in accuracy, sensitivity, specificity and reproducibility of the findings.

Automatic component identification method for ICA on resting-state fMRI and its feasibility to presurgical planning

Task-based fMRI has been widely used to guide presurgical mapping non-invasively. Application of resting-state fMRI (rs-fMRI) was recently reported in as well (Zhang et al., 2009) as patient in severe clinical conditions may not be able to perform tasks. Independent component analysis (ICA)-based mapping (Kokkonen et al., 2009) shows its advantage as no a priori information is required, which naturally fits for rsfMRI. However, such issues should not be neglected that components identified by subjective visual inspection often leads to bias or errors; ICA also faces inconsistency problems; previous findings based on case study may lack of validity. Therefore, study based on a large group and optimized ICA algorithm is warranted for mapping eloquent cortex in neurosurgery. 1Center for Cognition and Brain Disorders, Affiliated Hospital, Hangzhou Normal University, Hangzhou, China, 2Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.

Determining Remaining Strength Capacity of Deteriorating RC Bridge Substructures

Condition-ratings in bridges have been used by the Federal Highway Administration as a way to classify structural deficiencies and structures that are functionally obsolete. These ratings are determined by evaluating the severity of the deterioration or nonconforming issues in affected bridges. The quality of the condition-rating practice is subjective (visual inspection based on the inspector’s view) and time delayed due to inspection cycles, which are scheduled for every two years. However, some deterioration may not be visible to the inspector. For instance, concrete substructures exposed to deicing salts or those in marine environments may begin experiencing deterioration or corrosion without showing any external damage. In addition, once a concrete substructure begins to experience these types of defects, the substructure’s durability becomes compromised. Consequently, it is important to evaluate damaged substructures and analyze the severity of the substructure’s degradation, the decrease in performance over time. This paper uses a risk-based approach (a nondestructive evaluation technique) to calculate and analyze some probabilities of failure associated with degradation factors. Overall, the objective of this research was to develop a generalized procedure, through the integration of deterministic and stochastic durability design methods, which analytically evaluates the durability of deteriorated reinforced concrete (RC) substructures. Furthermore, a decision tree is created to help inspectors exemplify the current condition or remaining strength of concrete substructures that have experienced degradation.

Applications of computer vision on tile alignment inspection

Abstract Subjective visual inspection is the main quality measure for tile alignment acceptance because of a lack of standard operation procedures in Taiwan. Without quantitative specifications for tile alignment, inspectors can easily manipulate the outcome; therefore, fine craftsmanship is not valued, resulting in considerable quality variation in tile installation works. This paper proposes an automated tile installation quality assurance prototype system that uses computer vision technologies. The system receives digital images of finished tile installation and has the images processed and analyzed to capture the geometric characteristics of the finished tile surface. The geometric characteristics are subsequently evaluated to determine the quality level of the tiling work. Application of the proposed automated system can effectively improve the tile alignment inspection practice and simultaneously reduce improper manipulation during acceptance procedures.

Diagnostic assessment of different environmental cleaning monitoring methods

In a simultaneous assessment of 250 environmental surfaces after terminal cleaning using aerobic cultures as a gold standard, both fluorescent marker and an adenosine triphosphate bioluminescence assay system demonstrated better diagnosticity compared with subjective visual inspection. These results support the use of these environmental monitoring systems in the health care setting.

Different mandibular first molar shapes according to groove and cusp configuration in relation to suggested bracket position

The aim of this study was to explore the shape differences in the mandibular first molars through orthographic measurements using three-dimensional (3D) virtual models, and study the possibility of new morphologic categories that require more than subjective visual inspection. A total of 164 mandibular first molars with five cusps were selected for classification. Using 3D laser scanning and reconstruction software, virtual casts were constructed. After several linear and angular measurements on the virtual occlusal plane, the teeth were clustered using the partitioning around medoids methods-an unsupervised classification. The cluster analysis presented two clusters that showed statistically significant differences in the measurements over the cusp locations and groove configurations. However, gender differences were not shown in the angular groove and cusp configurations. Two clusters were found in the population of the present study, and this result suggested the existence of a diverse morphologic trait in the mandibular molar even in the same origin and could be considered in positioning orthodontic brackets that have built-in prescriptions.

Improved Needle Visualization With Electronic Beam Steering

The objective of this study was to evaluate electronic beam steering to improve needle visualization. Various needles ranging from 27 to 12 gauge were evaluated with a modified CX50 (Philips Ultrasound, Bothell, Wash) using a 12-MHz linear probe. For proof of concept, the needles were placed in a pork roast at various random angles. The ultrasound beam was electronically steered at angles from 0 to 50 degrees. Images were obtained at the various angles and compared. Two patients undergoing a thyroid biopsy, one patient undergoing biopsy of the supraclavicular node, and one undergoing a breast biopsy were evaluated using electronic beam steering at various angles. The images were stored and subjectively compared. All needles were better visualized when the ultrasound beam was steered as close to perpendicular to the needle as possible. Significant improvements in needle including tip visualization by subjective visual inspection were found by applying the appropriate beam angle. Electronic beam steering has the potential to significantly improve needle visualization.

Defect detection and quantification system to support subjective visual quality inspection via a digital image processing: A tiling work case study

Abstract Several areas of the quality inspection on architectural works only use subjective visual inspection, especially those involving aesthetic faults. Because people are limited in what they can detect visually, inspectors are not able to quantify the value of a defect and cannot perform an inspection that includes all possible defects. Subjective evaluations depend on individual experience and are unreliable because they do not have objective standards. Therefore, this paper presents an innovative system of defect detection and quantification. The system is able to augment subjective visual quality inspections in architectural work by specifying defect positions and quantifying defect values. This method uses defect feature analysis and quantifies the defect value from digital images using a digital image processing technique. The inspection of tiling work was chosen as a case study for developing a prototype of the system. This paper describes the conceptual framework of the proposed system's application and the methodology of the system's development. It includes a field verification of the potential and accuracy of developed prototype system by comparing the results of human inspections and those of the proposed system. A potential benefit of the system is an increase in the reliability of visual quality inspection by reducing the subjective human judgment of aesthetic faults.

Dual Mode Sensing with Low-Profile Piezoelectric Thin Wafer Sensors for Steel Bridge Crack Detection and Diagnosis

Monitoring of fatigue cracking in steel bridges is of high interest to many bridge owners and agencies. Due to the variety of deterioration sources and locations of bridge defects, there is currently no single method that can detect and address the potential sources globally. In this paper, we presented a dual mode sensing methodology integrating acoustic emission and ultrasonic wave inspection based on the use of low-profile piezoelectric wafer active sensors (PWAS). After introducing the research background and piezoelectric sensing principles, PWAS crack detection in passive acoustic emission mode is first presented. Their acoustic emission detection capability has been validated through both static and compact tension fatigue tests. With the use of coaxial cable wiring, PWAS AE signal quality has been improved. The active ultrasonic inspection is conducted by the damage index and wave imaging approach. The results in the paper show that such an integration of passive acoustic emission detection with active ultrasonic sensing is a technological leap forward from the current practice of periodic and subjective visual inspection and bridge management based primarily on history of past performance.

Improved quality control processing of peptide-centric LC-MS proteomics data.

Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values.Results: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs.Availability: https://www.biopilot.org/docs/Software/RMD.phpContact: bj@pnl.govSupplementary information: Supplementary material is available at Bioinformatics online.

A new method for evaluating regional air quality forecasts

Abstract A Kriging interpolation method is combined with an object-based evaluation measure to assess the ability of the UK Met Office’s dispersion and weather prediction models to predict the evolution of a plume of tracer as it was transported across Europe. The object-based evaluation method, SAL, considers aspects of the Structure, Amplitude and Location of the pollutant field. The SAL method is able to quantify errors in the predicted size and shape of the pollutant plume, through the structure component, the over- or under-prediction of the pollutant concentrations, through the amplitude component, and the position of the pollutant plume, through the location component. The quantitative results of the SAL evaluation are similar for both models and close to a subjective visual inspection of the predictions. A negative structure component for both models, throughout the entire 60 h plume dispersion simulation, indicates that the modelled plumes are too small and/or too peaked compared to the observed plume at all times. The amplitude component for both models is strongly positive at the start of the simulation, indicating that surface concentrations are over-predicted by both models for the first 24 h, but modelled concentrations are within a factor of 2 of the observations at later times. Finally, for both models, the location component is small for the first 48 h after the start of the tracer release, indicating that the modelled plumes are situated close to the observed plume early on in the simulation, but this plume location error grows at later times. The SAL methodology has also been used to identify differences in the transport of pollution in the dispersion and weather prediction models. The convection scheme in the weather prediction model is found to transport more pollution vertically out of the boundary layer into the free troposphere than the dispersion model convection scheme resulting in lower pollutant concentrations near the surface and hence a better forecast for this case study.