Manual Marking Research Articles

Invading Testing System Based on Clustering and HMM Distributed

In the invading testing, the testing of unknown is mainly accomplished by the abnormal testing. Traditional abnormal testing methods need to construct a normal behavior feature outline reference mode. When establish this mode, it is needed to have large amount of pure normal data set, and this data set usually is not easy to gain from the real network. Whats worse, the problem of too much error reports and leaking reports in the abnormal testing is pervasive. In order to overcome this shortage, this paper rises a abnormal testing method which is combine clustering analysis and HMM. This method doesnt need any training data set of manual marking; it can explore many different types of invading behaviors. The experimental results indicate that this method has better effect on the testing, which is of a higher testing rate and lower error report rate.

Evaluation of Possible Error Sources in Corneal Endothelial Morphometry With a Semiautomated Noncontact Specular Microscope

To assess the corneal endothelium, particularly the polymegethism feature, using the Topcon SP-3000P specular microscope with newer center-dot software. Forty-eight healthy, normal weight, noncontact lens wearers of Asian ethnicity were assessed. Single endothelial images from each subject were processed with center-dot software, reevaluated after correction of obvious errors, and then by manual border marking and planimetry. Endothelial cell density based on average cell area and the coefficient of variation (COV) of cell area (polymegethism) were calculated. Error sources are associated with erroneous location of cell borders (usually creating larger or smaller "cells") or failure to assign cell borders to a marked cell. On the initial application of the center-dot software, the endothelial cell density values ranged from 1822 to 3244 cells per square millimeter (mean, 2644 cells/mm); this range was reduced (eg, 1955-3054 cells/mm; mean 2690 cells/mm on editing or in manual planimetry). The COV values ranged from 17% to 39% (mean, 27.5% ± 5.5%), with one third of the endothelia yielding COV values of greater than or equal to 30%. On editing or in manual planimetry, the COV values were reduced to between 17% and 29% (mean, 24.5% ± 3.2%; P < 0.001). In the use of a center-dot endothelial analysis program with cell border identification, it is likely that at least 1 set of editing steps is required to produce reasonable results.

Accurate semiautomatic assessment of ligament length variations from MRI data

A semiautomatic method for the assessment of ligament length variations during different joint positions based on MRI data is proposed. Ligament lengths are represented as distances between points marking characteristic locations in the ligament insertion regions on the bones. These points are defined manually for one single reference joint position and for all other joint positions they are automatically mapped with high accuracy to the correct locations using image registration methods. The methodology is validated using data from 16 volunteers depicting the coracoclavicular ligaments in the left shoulder during different arm abductions. The method yielded a superior reproducibility of the point locations over different joint positions compared to manual point marking. Significant ligament length variations were found for different abductions which was not possible with manual measurements. Acquisition related geometric distortions and inaccuracies during the registration and segmentation process were small. The proposed method provides superior accuracy for the in vivo analysis of ligament dynamics compared to manual measurements. This permits a better understanding of the ligament behavior during joint motion and offers new possibilities for presurgical planning which to date has not been possible with manual data analysis.

Automatic assessment of Java code

In this study a tool was developed which marks automatically computer programs written by novices in Java. Existing open source static analysers were evaluated in the development. Metrics and marking schemes were developed to mark and grade the programming assignments automatically. A sample of 59 first-year programming students’ projects was selected for marking. The projects were marked manually and automatically. The results showed that there is a high correlation between manual and automatic marking for all criteria. The mean Kendall’s tau is over 0.75 indicating a very high level of correlation between manual and automatic marking. However, depending on the marking criteria, non-systemic variations were found.

Automated aorta segmentation in low-dose chest CT images

Abnormalities of aortic surface and aortic diameter can be related to cardiovascular disease and aortic aneurysm. Computer-based aortic segmentation and measurement may aid physicians in related disease diagnosis. This paper presents a fully automated algorithm for aorta segmentation in low-dose non-contrast CT images. The original non-contrast CT scan images as well as their pre-computed anatomy label maps are used to locate the aorta and identify its surface. First a seed point is located inside the aortic lumen. Then, a cylindrical model is progressively fitted to the 3D image space to track the aorta centerline. Finally, the aortic surface is located based on image intensity information. This algorithm has been trained and tested on 359 low-dose non-contrast CT images from VIA-ELCAP and LIDC public image databases. Twenty images were used for training to obtain the optimal set of parameters, while the remaining images were used for testing. The segmentation result has been evaluated both qualitatively and quantitatively. Sixty representative testing images were used to establish a partial ground truth by manual marking on several axial image slices. Compared to ground truth marking, the segmentation result had a mean Dice Similarity Coefficient of 0.933 (maximum 0.963 and minimum 0.907). The average boundary distance between manual segmentation and automatic segmentation was 1.39 mm with a maximum of 1.79 mm and a minimum of 0.83 mm. Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.

The spatial rotator

This paper presents a new local volume estimator, the spatial rotator, which is based on measurements on a virtual 3D probe, using computer assisted microscopy. The basic design of the probe builds upon the rotator principle which requires only a few manual intersection markings, thus making the spatial rotator fast to use. Since a 3D probe is involved, it is expected that the spatial rotator will be more efficient than the the nucleator and the planar rotator, which are based on measurements in a single plane. An extensive simulation study shows that the spatial rotator may be more efficient than the traditional local volume estimators. Furthermore, the spatial rotator can be seen as a further development of the Cavalieri estimator, which does not require randomization of sectioning or viewing direction. The tissue may thus be sectioned in any arbitrary direction, making it easy to identify the specific tissue region under study. In order to use the spatial rotator in practice, however, it is necessary to be able to identify intersection points between cell boundaries and test rays in a series of parallel focal planes, also at the peripheral parts of the cell boundaries. In cases where over- and underprojection phenomena are not negligible, they should therefore be corrected for if the spatial rotator is to be applied. If such a correction is not possible, it is needed to avoid these phenomena by using microscopy with increased resolution in the focal plane.

Automated x‐ray/light field congruence using the LINAC EPID panel

X-ray/light field alignment is a test described in many guidelines for the routine quality control of clinical linear accelerators (LINAC). Currently, the gold standard method for measuring alignment is through utilization of radiographic film. However, many modern LINACs are equipped with an electronic portal imaging device (EPID) that may be used to perform this test and thus subsequently reducing overall cost, processing, and analysis time, removing operator dependency and the requirement to sustain the departmental film processor. This work describes a novel method of utilizing the EPID together with a custom inhouse designed jig and automatic image processing software allowing measurement of the light field size, x-ray field size, and congruence between them. The authors present results of testing the method for aS1000 and aS500 Varian EPID detectors for six LINACs at a range of energies (6, 10, and 15 MV) in comparison with the results obtained from the use of radiographic film. Reproducibility of the software in fully automatic operation under a range of operating conditions for a single image showed a congruence of 0.01 cm with a coefficient of variation of 0. Slight variation in congruence repeatability was noted through semiautomatic processing by four independent operators due to manual marking of positions on the jig. Testing of the methodology using the automatic method shows a high precision of 0.02 mm compared to a maximum of 0.06 mm determined by film processing. Intraindividual examination of operator measurements of congruence was shown to vary as much as 0.75 mm. Similar congruence measurements of 0.02 mm were also determined for a lower resolution EPID (aS500 model), after rescaling of the image to the aS1000 image size. The designed methodology was proven to be time efficient, cost effective, and at least as accurate as using the gold standard radiographic film. Additionally, congruence testing can be easily performed for all four cardinal gantry angles which can be difficult when using radiographic film. Therefore, the authors propose it can be used as an alternative to the radiographic film method allowing decommissioning of the film processor.

Automatic Marking about T­-SQL Based on One­way Close Degree

This paper puts forward a method to realize Transact­SQL (T­SQL) automatic test paper evaluation, using one­way close degree to calculate the degree of similarity between the candidate answer and standard answer to realize automatic evaluation. And it introduces the grammar and structure of T­SQL briefly, details the definition of one­way close degree, and analyzes and realizes the one­way close degree algorithm. The comparison of automatic marking and manual marking examples shows that, automatic marking is close to manual marking.

Automatic Measurement in Large-Scale Space with the Laser Theodolite and Vision Guiding Technology

The multitheodolite intersection measurement is a traditional approach to the coordinate measurement in large-scale space. However, the procedure of manual labeling and aiming results in the low automation level and the low measuring efficiency, and the measurement accuracy is affected easily by the manual aiming error. Based on the traditional theodolite measuring methods, this paper introduces the mechanism of vision measurement principle and presents a novel automatic measurement method for large-scale space and large workpieces (equipment) combined with the laser theodolite measuring and vision guiding technologies. The measuring mark is established on the surface of the measured workpiece by the collimating laser which is coaxial with the sight-axis of theodolite, so the cooperation targets or manual marks are no longer needed. With the theoretical model data and the multiresolution visual imaging and tracking technology, it can realize the automatic, quick, and accurate measurement of large workpieces in large-scale space. Meanwhile, the impact of artificial error is reduced and the measuring efficiency is improved. Therefore, this method has significant ramification for the measurement of large workpieces, such as the geometry appearance characteristics measuring of ships, large aircraft, and spacecraft, and deformation monitoring for large building, dams.

Real‐time automatic fiducial marker tracking in low contrast cine‐MV images

To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images. The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.

Quantitative Analysis of Ultrasound Contrast Flow Behavior in Carotid Plaque Neovasculature

Intraplaque neovascularization is considered as an important indication for plaque vulnerability. We propose a semiautomatic algorithm for quantification of neovasculature, thus, enabling assessment of plaque vulnerability. The algorithm detects and tracks contrast spots using multidimensional dynamic programming. Classification of contrast tracks into blood vessels and artifacts was performed. The results were compared with manual tracking, visual classification and maximal intensity projection. In 28 plaques, 97% of the contrast spots were detected. In 89% of the objects, the automatic tracking determined the contrast motion with an average distance of less than 0.5 mm from the manual marking. Furthermore, 75% were correctly classified into artifacts and vessels. The automated neovascularization grading agreed within 1 grade with visual analysis in 91% of the cases, which was comparable to the interobserver variability of visual grading. These results show that the method can successfully quantify features that are linked to vulnerability of the carotid plaque.

TU-E-BRA-04: Real-Time Automatic Fiducial Marker Detection in Low Contrast Cine-MV Images.

Intrafraction motion tracking using beam-line MV images have gained much attention because no additional imaging dose is introduced. Since MV images have much lower contrast than kV images, a robust marker detection algorithm is a pre-requisite. In this work, we develop a novel, fast, and robust method to detect implanted markers in low-contrast cine-MV patient images. Several marker detection methods have been proposed in the recent years. These methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover the different situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the ROI. We solve this problem by synergetic use of modern but well-tested computer vision and AI techniques - detect implanted markers utilizing discriminant analysis for initialization and mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated using 1149 cine-MV images from 2 prostate IMRT patients and compared with manual marker detection results from 6 researchers. The average of the manual detection results is considered as the ground truth. The average RMS errors of the automatic tracking from the ground truth are 1.9 and 2.1 pixels for the 2 patients (0.26mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed method can achieve similar marker detection accuracy to manual detection in low-contract cine-MV images.

Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson’s disease

Images captured during routine clinical transcranial sonography (TCS) examination are of a low resolution, so can be confusing for diagnostic evaluations. Manual segmentation of brain structures (areas of the midbrain and substantia nigra (SN)) that are of special interest cause inter-observer and intra-observer variability, thus restricting the reliability of Parkinson disease (PD) diagnostics. This paper presents a new technique for automated segmentation applicable to low resolution sonographic images, and particularly to brain structures related to PD. The segmentation was performed by a modified shape-based active contour (AC) segmentation algorithm. In order to suppress the speckle noise and to improve the AC segmentation, a pre-processing technique based on the averaging of adjusted spatially varying TCS images is proposed. The latter technique was tested on clinical TCS images. The results of the automated segmentation were compared with the manual markings. Two experts on the 40TCS images performed these markings. The comparison showed that an automated method is effective when segmentation of the midbrain is performed (averaged overlap between regions obtained automatically and outlined manually was 73.10±7.45%). The results of the segmentation of the SN area showed that a sufficiently correct contour of this area could also be obtained, but the accuracy of the segmentation is related to the image quality. It should be emphasised that the main difficulty in evaluating the accuracy of automated segmentation of the SN was the indefinite “gold standard” (variation between the measurements of two experts with different experience was found). And, therefore, the diagnostic reliability of the proposed technique was inconclusive.

Research of Flash Automatic Marking Based on Logical Formal

The author proposes a scheme of flash automatic marking based on animation effects. Describe the question's marking information by using the logical formal method. Achieve automatic marking by building the logical formal system. Focuses on two components of the scheme: logical formal description and SWF to XML. Describes the whole process of logical formal marking with example. Analysis of manual and automatic marking shows that: logical Formal automatic marking error is better than the manual average error.

Marking Extended Essays on Screen: Exploring the Link between Marking Processes and Comprehension

Technological developments are impacting upon UK assessment practices in many ways. For qualification awarding bodies, a key example of such impact is the ongoing shift towards examiners marking digitally scanned copies of examination scripts on screen rather than the original paper documents. This digitisation process has obvious benefits, affording flexible information management and distribution. Digitisation can also lead readers to interact differently with texts, with readers' physical engagement with digital texts differing from traditional paper-based texts. More specifically, it appears that reader navigation and annotation behaviours are particularly influenced by a digital mode shift. A consequence of this is that it is possible that the mental workload involved in reading on screen is greater than when reading on paper. Moreover, it is theorised that this additional load can have an adverse affect on reader comprehension building. These findings prompt questions about whether the mode of marking might unduly influence marking outcomes when assessors read longer essay responses in a digital environment. In light of this concern, this article explores how marking mode influences examiner essay marking behaviours, which in turn might influence their comprehension building and their marking outcomes. This study gathers data from 12 experienced examiners working within a large UK-based awarding body to investigate how mode influences their navigation and annotation behaviours when marking extended essays on paper and on screen. These comparative data are then used to explore a model which suggests that manual marking behaviours, such as navigation and annotation, can influence examiners' comprehension whilst marking. The implications for future developments in digital essay-marking software are then discussed.

Evaluation of accuracy and reproducibility of the optical measuring system in cast machine tool body assessment

Evaluation of accuracy and reproducibility of the optical measuring system in cast machine tool body assessment The hereby presented study puts forth the fundamentals of an innovative technology enabling rapid assessment of machine tool cast shape by elimination of manual marking out requirement and machining surplus minimization. The new technique is based on optical measuring system utilization for the design of virtual cast models and their comparative analysis with structural models. Two small scale machine tool body casts were selected for the investigation. The measurements were conducted in triplicate series for each cast labeled with reference markers, by means of Atos GOM II optical scanner. For further comparison, one of the casts was additionally scanned without labeling. Flatness parameters of selected cast surfaces were determined for geometric accuracy evaluation and the scanned cast shapes were compared with reference models. The comparison results were recorded as multicolored maps projected upon the experimental cast and reference model surfaces. Practical map interpretation was further elucidated and the surplus sizes on the machined surfaces were assessed accordingly. Comparative analyses of individual models were demonstrated for all measurement series of both casts, affording reproducibility evaluation of optical scanning system measurements. Economic viability of the proposed technology market implementation was unequivocally established, as it provides for considerable reduction in the cast machining scale as well as the quality control of 100%.

Making Movies: The Next Big Thing in Feedback?

AbstractGood quality, timely feedback is a key factor to help students achieve their full potential. Increased class sizes have put significant strain on the ability to return work promptly without compromising feedback quality. In the current study, two screencasting technologies were used to produce audiovisual feedback. For essays, Jing was used, alongside a sample that were marked using typed comments in GradeMark for comparison. For longer lab reports, Camtasia Studio 7 software was used, allowing for editing and inclusion of additional material. Screencasting essay feedback reduced overall marking time by up to 50% compared to the typed comments, whilst the edited screencasts took about the same time as manual marking - although the editing time could probably be reduced. The students’ responses to the audiovisual feedback were extremely positive, with evidence of deeper engagement with the feedback and greater understanding of the tutor’s comments. Screencasting should thus be seen as a potentially...

Research of SQL Server Automatic Marking Based on Logical Formal

The author proposes a scheme of SQL Server Automatic Marking based on logical formal. The scheme consists of three parts: generating paper, skill assessment and automatic marking. Describe the question's Marking information by using the logical formal method. Achieve automatic Marking by building the logical formal system. Focus on three components of the scheme: logical formal description, get information of database based on ADOX(Microsoft ActiveX Data Objects Extensions for Data Definition Language and Security) and automatic marking of three database operations:Creat,Modify and Delete. Describes the whole process of logical formal Marking with example. Analysis the assessment effect of the results about the example. Analysis between manual and automatic Marking shows that: logical Formal automatic Marking is better than the average artificial error and close to the minimum artificial error.

GA-based multiresolution fusion of segmented brain images using PD-, T1- and T2-weighted MR modalities

Medical image fusion has been used to derive the useful complimentary information from multimodality imaging. The proposed methodology introduces fusion approach for robust and automatic extraction of information from segmented images of different modalities. This fusion strategy is implemented in multiresolution domain using wavelet transform- and genetic algorithm-based search technique to extract maximum complementary information. The analysis of input images at multiple resolutions is able to extract more fine details and improves the quality of the composite fused image. The proposed approaches are also independent of any manual marking or knowledge of fiducial points and start the fusion procedure automatically. The performance of fusion scheme implemented on segmented brain images has been evaluated computing mutual information as similarity measuring matrix. Prior to fusion process, images are being segmented using different segmentation techniques like fuzzy C-mean and Markov random field models. Experimental results show that Gibbs- and ICM-based segmentation approaches related to Markov random field perform over the fuzzy C-mean and which are being used prior to GA-based fusion process for MR T1, MR T2 and MR PD images of section of human brain.

SU‐E‐J‐51: Automatic Detection of Markers Based on Threshold Clustering in 3D CT Images for Image‐Guided Radiation Therapy

Purpose: Marker‐based registration, needed especially when there is no sufficient bony anatomy for adequate match, plays an important role in the image guided radiation therapy (IGRT) systems, of which the accuracy of registration greatly depends on that of the location of the markers embed. Since the manual marker detection has many limits for its time consuming and labor intensive, it is essential to enable an automatic detection of makers to help to cut down the human error in registration as well as to get a speedup of the registration process, making IGRT systems more efficient. Methods: In this study, we present a simple and efficient clustering method based on the k‐means clustering algorithm, which we call threshold‐based clustering. This method is established in three procedures. First, we got a set of segmented 3D CT images using binary thresholding method since the value of the markers is far different from that of the CT images. Second, the calculation was done by comparing the spacial distance between every two possible marker candidates to make marker points with similar intensity clustered. Finally, the centers of markers can be obtained after a given number of iteration time which can be changed according to the prior estimated number of markers. Results: This method has been tested on 46 CT slices with a size of 512*512. Markers were successfully identified in at least 99.00%. It took less than a second to detect three markers with the average location accuracy of 0.56mm compared to 1.50mm for the conventional manual technique. Conclusions: It has been testified that the method proposed can accurately detect the location of the markers within 3D CT images. Our results demonstrate the subvoxel accuracy can be achieved completely automatically which makes the method well suited for clinical applications.Supported by the National Natural Science Foundation under grant No.30900386 and the Anhui Provincial Natural Science Foundation under grant No. 090413095 and 11040606Q55