Automatic alignment method for calibration of hydrometers

We propose a self-learning method for automatic wafer alignment in the semiconductor manufacturing process. A feed forward neural network is trained by and used for wafer alignment. The network determines the movement of kinematic parts from the misalignment inspected by machine vision. However, it is time-consuming and inconvenient to obtain training data in this way. So, we built an automatic learning rule to gather the data and train the network. The network may determine wrong outputs and cause other misalignments at first, but the error can decrease as the training proceeds. The training sets consisted of a variation of misalignment data and the movement of an alignment stage. Five recent sets are used for training and others are dismissed or forgotten. This retrained network tried aligning, measured misalignment, and made new training sets. This sequence makes it possible to acquire alignment skill and automate the process. After learning, automatic alignment accomplished sub-pixel accuracy for several cases of misalignment. The result showed that the proposed method could be applied to the semiconductor manufacturing process. Its performance improved about 6% compared with conventional algorithms.

Markov random field based automatic image alignment for electron tomography

Purpose. The aim of this study is to develop software to automatically assess the laser alignment on the ACR CT phantom and evaluate its accuracy on sixteen CT scanners. Methods. Software for an automated method of laser alignment assessment on the ACR CT phantom was developed. Laser alignment assessment was based on the positions of the ball-bearing markers at the edge of the ACR CT phantom. The automatic assessment was performed using several steps, including segmentation to acquire the coordinates of the ball-bearing markers and determination of the distances between lines connecting them with lines through the center of the image. A comparison of the results from the automatic method with those from the manual method was performed. The manual measurements were carried out using MicroDicom Viewer. A Mann–Whitney U test was performed to determine the statistical difference between both methods. The evaluation was performed on images of the ACR CT phantom scanned with 16 CT scanners from 5 different CT manufacturers. Results. The results confirmed that our software successfully segments the ball-bearing markers and determines the laser alignment assessment on the ACR CT phantom. Evaluation of the algorithm with images from the 16 CT scanners revealed that the difference between the results from automatic and manual methods were about 0.2 mm with a p value of around 0.7 (no statistical difference). Misalignment in the y-axis was larger than the misalignment in the x-axisfor the majority of the scanners tested. It was found that the phantom tended to be placed 2 mm higher than the iso-center. Conclusions. Software to automatically assess CT laser alignment with the ACR CT phantom was successfully developed and evaluated. The automatic assessment was comparable to manual assessment. In addition, the automatic method was user independent and fast.

XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis

Local feature based methods have achieved a great success in the field of image matching due to its invariance under typical image transformations. However, local features are often not invariant under complex non-affine transformations, which makes the matching methods ineffective. To remove the effect of complex image transformations, this paper proposes alignment of images before they are compared. An automatic image alignment method is introduced based on thin plate spline (TPS) warping. Then, the aligned matching scheme is designed to utilize correct alignments and reject false alignments for the improvement of matching performance. The method is evaluated using scene retrieval and object categorization. Experiments show the proposed aligned matching outperforms two typical methods: voting scheme and histograms comparison (over a set of prototypes which must be found by clustering).

The Semantic Web is based on technologies that make the content of the Web machine-understandable. In that framework, ontological knowledge representation has become an important tool for the analysis and understanding of multimedia information. Because of the distributed nature of the Semantic Web however, ontologies describing similar fields of knowledge are being developed and the data coming from similar but non-identical ontologies can be combined only if a semantic mapping between them is first established. This has lead to the development of several ontology alignment tools. We propose an automatic ontology alignment method based on the recursive neural network model that uses ontology instances to learn similarities between ontology concepts. Recursive neural networks are an extension of common neural networks, designed to process efficiently structured data. Since ontologies are a structured data representation, the model is inherently suitable for use with ontologies.

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a self-supporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called “smart feet”. Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm.

The present paper studies how to calibrate hydrometer using image process. The method aligns particular scales of hydrometer selected for calibrating the hydrometer with the horizontal plane of the reference liquid automatically without man`s operation. Major parts composing the system are CCD camera, frame grabber, stepping motor and image process program. The image process program is composed of a part that locates the meniscus and aligns it with a scale and a part that controls the step motor. To verify the performance of the developed method, this study compares the meniscus and scale observed directly with the naked eye with the result of calibration by the manual calibration method. The differences between the corrections were less than with uncertainty of . These showed that the calibration results of the developed hydrometer calibration using image process nearly equal to manual method.

Background: Multislice Computed Tomography (MSCT) is utilized for the measurement of bleeding volume in hemorrhagic stroke patients. This is very necessary to determine the right medical action. Errors in measurement can lead to suboptimal medical measures taken. Objective: to analyze the measurement and calculation of fluid volume by automatic and manual methods on the volume of fluid contained in the phantom on MSCT. Methods: The type of research is quantitative experimental with the sample used is a phantom made of polyethylene (Dacron) filled with irregular milk liquid as much as 10 volume variations. The research instruments used were worksheets and interview sheets. Data processing and analysis are descriptive by getting the average value. Results: Measurement and calculation of irregular volume of 10 ml, 20 ml, 30 ml, 40 ml, 50 ml, 60 ml, 70 ml, 80 ml, 90 ml, and 100 were done automatically on the CT scan plane and manually using the formula ½ A x B x C. The automatic method had an average volume difference of 1.39% and the manual method had an average volume difference of 6.88%. The automatic method has a small volume difference. Conclusion: the measurement and calculation of volume with the automatic method has a better accuracy value than the manual method. The results of these measurements and calculations are highly dependent on the accuracy of precise segmentation techniques on the object.

Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

In this study, in vitro blood flowing through a 100 μm glass capillary was studied. The images were captured using a confocal system and post-processed using Image J and MatLab. The aim of the present work, was to measure the trajectories of the cell-free layer (CFL) by using two different methods, i. e., a manual method (MM) and an automatic method (AM). For the MM we have used amanual tracking plugin (MTrackJ) from Image J to track labeled red blood cells (RBCs) flowing around the boundary of the RBCs core. For the AM we have used a MatLab scripts to measure automatically the CFL trajectories. The preliminary numerical results suggest that the CFL trajectories follow a polynomial function for both methods.

This paper describes an investigation into the reliability of an automatic assessment method of the learner-build concept map by comparing it with two well-known manual methods. We have previously proposed the Kit-Build (KB) concept map framework where a learner builds a concept map by using only a provided set of components, known as the set “kit”. In this framework, instant and automatic assessment of a learner-build concept map has been realized. We call this assessment method the “Kit-Build method” (KB method). The framework and assessment method have already been practically used in classrooms in various schools. As an investigation of the reliability of this method, we have conducted an experiment to compare the assessment results of the method with the assessment results of two other manual assessment methods. In this experiment, 22 university students attended as subjects and four as raters. It was found that the scores of the KB method had a very strong correlation with the scores of the other manual methods. The results suggest that automatic assessment of the Kit-Build concept map can attain almost the same level of reliability as well-known manual assessment methods.

Many diseases such as anemia and leukemia are detected by counting Red Blood Cells (RBCs or erythrocytes). Generally, there are manual and automatic method for RBCs counting. In the manual method, RBCs counting is performed by a hematologist with the help of special medical equipment. The manual method is tedious, time-consuming and dependent on equipment and hematologist's expert and accuracy. The automatic method is fast, high accuracy and independent of equipment and hematologist. It is performed with the assistance of a microscopic image of the person's blood and a computer system. In this paper, a new method is presented for counting incomplete or cropped RBCs by Circular Hough Transform (CHT) and another method called EndPoints method which will be described in the following. Two parallel tasks are performed on the input image. In the first work, the input image is segmented using thresholding and erosion on the green channel. In the second work, the input image is converted to a grayscale image and edged by the Canny method. Then, the segmented image is subtracted from the edged image and remove White Blood Cells (WBCs or leucocytes) from it. After margin is added, by defining EndPoints set estimated continuation of RBCs will be added to the image. Finally, CHT is applied to the image and it will calculate the number of RBCs. Also, new counting error and counting accuracy metrics are defined for counting problem. The method is evaluated on the fifteen images of the ALL-IDB1 database and achieved 97.14 overall accuracy.

Objective To reduce labor intensity and increase homogeneity of varicella vaccine by application of automatic system for preparation of semi-finished products. Methods The dilution of varicella vaccine bulk and filling of semi-finished product were conducted by either the automatic system or a manual method. The cleaning, sterilizing and insulating effects of containers and verification results of semi-finished and final products were compared between the two methods. Results Automatic operation reduced 60% labor and 80% work time. The cleaning and sterilizing effects of container or tank by both methods complied with process requirements. The temperature maintenance was well controlled by the automatic method. Difference among the virus titers of semi-finished products produced by automatic system was much smaller as compared with manual method, with coefficient of variation at 0.00% and 2.17%, respectively. The stability test results of final products met the titer requirement —no less than 3.3 lg plaque-forming unit (PFU)/0.5 ml. The virus titers of final products produced by automatic and manual methods were 3.7 and 3.6 lgPFU/0.5 ml at 37 ℃ for 7 d, and 3.5 and 3.4 lgPFU/0.5 ml at 2-8 ℃ for 18 months. Conclusion The use of automatic system for bulk dilution and semi-filling can reduce labor intensity and improve homogeneity of the product. Key words: Chickenpox vaccine; Automation; Technology, pharmaceutical; Drug stability

An automatic version of the methylene-blue procedure for determining alkylbenzenesulphonates in fresh and saline waters on the AutoAnalyzer is described. Results have been examined statistically and compared with those obtained by the manual methylene-blue method. Student's t-test indicates more than 90 per cent. probability that the manual and automatic methods will give the same value. The sampling rate is 14 per hour, and the standard deviation is 0·04 p.p.m. of Manoxol OT in the range 0 to 4 p.p.m. of Manoxol OT. Whilst chloride, sulphide and sulphate showed no interference when tested, peaty water did interfere with the method. However, the results obtained showed a correlation to the colour (in p.p.m. of platinum) of the water examined. Emulsification, which causes considerable error in the manual method, is completely avoided in the automatic method.