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Biomedical subjects

J Bijhold

Publications and source records attributed to J Bijhold.

10 recordsLinked to original sources
PubMed2001

Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms.

On the market several systems exist for collecting spent ammunition data for forensic investigation. These databases store images of cartridge cases and the marks on them. Image matching is used to create hit lists that show which marks on a cartridge case are most similar to another cartridge case. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for optimizing the results of image matching. The images are acquired by side light images for the breech face marks and by ring light for the firing pin impression. For these images a standard way of digitizing the images used. For the side light images and ring light images this means that the user has to position the cartridge case in the same position according to a protocol. The positioning is important for the sidelight, since the image that is obtained of a striation mark depends heavily on the angle of incidence of the light. In practice, it appears that the user positions the cartridge case with +/-10 degrees accuracy. We tested our algorithms using 49 cartridge cases of 19 different firearms, where the examiner determined that they were shot with the same firearm. For testing, these images were mixed with a database consisting of approximately 4900 images that were available from the Drugfire database of different calibers.In cases where the registration and the light conditions among those matching pairs was good, a simple computation of the standard deviation of the subtracted gray levels, delivered the best-matched images. For images that were rotated and shifted, we have implemented a "brute force" way of registration. The images are translated and rotated until the minimum of the standard deviation of the difference is found. This method did not result in all relevant matches in the top position. This is caused by the effect that shadows and highlights are compared in intensity. Since the angle of incidence of the light will give a different intensity profile, this method is not optimal. For this reason a preprocessing of the images was required. It appeared that the third scale of the "à trous" wavelet transform gives the best results in combination with brute force. Matching the contents of the images is less sensitive to the variation of the lighting. The problem with the brute force method is however that the time for calculation for 49 cartridge cases to compare between them, takes over 1 month of computing time on a Pentium II-computer with 333MHz. For this reason a faster approach is implemented: correlation in log polar coordinates. This gave similar results as the brute force calculation, however it was computed in 24h for a complete database with 4900 images.A fast pre-selection method based on signatures is carried out that is based on the Kanade Lucas Tomasi (KLT) equation. The positions of the points computed with this method are compared. In this way, 11 of the 49 images were in the top position in combination with the third scale of the à trous equation. It depends however on the light conditions and the prominence of the marks if correct matches are found in the top ranked position. All images were retrieved in the top 5% of the database. This method takes only a few minutes for the complete database if, and can be optimized for comparison in seconds if the location of points are stored in files. For further improvement, it is useful to have the refinement in which the user selects the areas that are relevant on the cartridge case for their marks. This is necessary if this cartridge case is damaged and other marks that are not from the firearm appear on it.

Algorithms↗
PubMed1992

Maximizing setup accuracy using portal images as applied to a conformal boost technique for prostatic cancer.

A design procedure of a patient setup verification protocol based upon frequent digital acquisition of portal images is demonstrated with an application for conformal prostatic boost fields. The protocol aims at the elimination of large systematic deviations in the patient setup and includes decision rules which indicate when correction of the patient setup is needed. The decision rules were derived from the results of a theoretical and quantitative analysis of patient setup variations measured in three pelvic fields (one anterior-posterior and two lateral fields) of 105 fractions for nine patients. Deviations in the patient positioning, derived from one field, were quantified as two-dimensional (2-D) displacement vectors in the plane perpendicular to the beam axis by alignment of anatomical features in the portal and the simulator image. The magnitude of the overall setup variations along the anterior-posterior, superior-inferior and lateral directions varied between 2.6 and 3 mm (1 S.D.). Inter- and intra-treatment variations could be separated, both having equal magnitudes of 1.7 to 2.2 mm (1 S.D.). In addition, intra-treatment variations appeared to be predictable which was a prerequisite for the development of the decision rules. The 2-D setup deviations, measured in the three fields of one fraction were strongly correlated and a 3-D displacement vector was calculated. Utilization of this 3-D vector in a setup verification protocol may lead to an early detection of systematic setup deviations.

Analysis of Variance↗
PubMed1991

Fast evaluation of patient set-up during radiotherapy by aligning features in portal and simulator images.

A new fast method is presented for the quantification of patient set-up errors during radiotherapy with external photon beams. The set-up errors are described as deviations in relative position and orientation of specified anatomical structures relative to specified field shaping devices. These deviations are determined from parameters of the image transformations that make their features in a portal image align with the corresponding features in a simulator image. Knowledge of some set-up parameters during treatment simulation is required. The method does not require accurate knowledge about the position of the portal imaging device as long as the positions of some of the field shaping devices are verified independently during treatment. By applying this method, deviations in a pelvic phantom set-up can be measured with a precision of 2 mm within 1 minute. Theoretical considerations and experiments have shown that the method is not applicable when there are out-of-plane rotations larger than 2 degrees or translations larger than 1 cm. Inter-observer variability proved to be a source of large systematic errors, which could be reduced by offering a precise protocol for the feature alignment.

Computer Simulation↗
PubMed1990

First clinical experience with a newly developed electronic portal imaging device.

In our institute an electronic portal imaging device (PID) has been developed and it recently became available for routine clinical practice. Images are available within 3 to 6 seconds after the start of irradiation; they are displayed on a video monitor next to the control console of the accelerator. The image quality is similar to the quality of images obtained with films. Because of its cassette-like shape and its low weight, the PID can easily be handled by technicians. An important advantage of the PID over conventional films is its pseudo-real time viewing facility. Typically, 5 to 10 images of each field can be made during one treatment session. In case a high accuracy in setup is demanded, the field edges of the first image, obtained with about 10% of the fraction dose, can be studied for acceptability before the rest of the dose is delivered. Using two prototype PID's first clinical experience has been obtained with patients treated for malignant tumors at various sites. Intra-treatment motion as a result of breathing, swallowing, or patient motion in a cast was seen. Motion of high contrast objects, for example, a field edge during irradiation, can be followed. This feature is important for future applications in computer controlled radiotherapy. Another advantage of the PID over film is that the image is digitally available. Therefore it can be further processed for quality improvement and quantitative analysis. Simple processing is done within seconds on the PID unit. A local network for the transfer of images from the accelerators to the evaluation room, where a detailed analysis of the field placement is performed, is under installation. Simulator film images are digitized in this room and are sent to the PID at the accelerator for a quick comparison with portal images during irradiation. We conclude that our device can replace the conventional film detector for portal imaging, that useful images are obtained within seconds during irradiation, and that the position of the field outline relative to the patient anatomy can be followed during dose delivery.

Electronics↗
PubMed1990

A method for the measurement of field placement errors in digital portal images.

Correct placement of radiation fields relative to patient anatomy is essential in radiotherapy in order to minimise serious side effects to reduce the probability of recurrence of the tumour. One way to determine patient setup accuracy is to analyse portal images obtained in the therapy beam distal to the patient. A field placement analysis (FPA) method has been developed for detailed evaluation of patient setup by comparing positions of corresponding radiation field edges in digitised simulator and portal images. A simulator image is matched to a portal image using similar anatomical landmarks in both images and mapping these landmarks against each other applying a least squares minimisation approach. Discrepancies between the simulator field edge (reference) and a portal field edge are determined by comparing the distances between the central axis of the beam and corresponding edge segments and the angles of these segments with a reference line. Uncertainties in these distances and angles are to a large extent determined by the magnification, rotation and translation procedure. Uncertainties due to the FPA method itself are of about 1.0 mm and 0.5 degrees in portal images of head and neck fields. These FPA uncertainties are in general smaller than the variations due to patient setups. Matching of simulator and portal images of lateral pelvic fields revealed larger uncertainties: 1.7 mm and 1.1 degrees. Setup variations in this kind of pelvic radiation field are usually also larger, and therefore meaningful results can be obtained with the new FPA method.

Computer Simulation↗
PubMedDate not supplied

Automatic verification of radiation field shape using digital portal images.

Two computer methods for matching digital line drawings have been tested for automatic on-line verification of the radiation field shape during radiotherapy. This work is part of a research program aiming at automated inspection of on-line acquired digital portal images. Both methods, moment normalization and point distance minimization, compare the field edge detected in the portal image with the intended field edge and the beam shaping devices marked in the simulator image. Tests showed that the methods should be used together. First, shape deviations in the detected field edge are classified quickly, in less than a second (25 MHz 386 + 387 PC), as large (e.g., missing blocks) or small (e.g., shifts of a few mm) by moment normalization. Then the portal image is mapped to the simulator image by field edge alignment with a translation and magnification obtained from moment normalization and a rotation from point distance minimization. The mapped portal image and the simulator image juxtaposed on a monitor screen for visual inspection. Finally, the small field shape deviations are detected by an analysis of the relationship between the radiation field shape and the positioning of field shaping devices using point distance minimization.

Humans↗
PubMedDate not supplied

Sampling methods for a matrix ionization chamber system.

To achieve increased image acquisition speed or better image quality, several read-out methods for a matrix ionization chamber system have been investigated. In this device, which is applied for portal imaging in radiation therapy, 256 x 256 small liquid-filled ionization chambers are scanned by switching the polarizing voltage applied to rows of chambers. The ionization current of each column is measured by a separate amplifier. In this approach, instead of measuring row by row, more complex switching schemes can be applied for the polarizing voltage. These schemes are useful either for varying speed and spatial resolution of the imaging device or for coded sampling of the ionization signal. The former option allows for doubling or quadrupling the acquisition speed with a small loss in image quality, or for obtaining a large improvement in signal-to-noise ratio at the cost of image resolution. In the latter option, coded sampling, the image is reconstructed mathematically from the measured signals. It is shown that in this case the application of Hadamard or derived matrices for sampling leads, under certain circumstances, to a noise reduction in the reconstructed image.

Humans↗
PubMedDate not supplied

Three-dimensional verification of patient placement during radiotherapy using portal images.

The use of a single portal image for the three-dimensional verification of patient placement during radiotherapy has been investigated. In this study, a deviation in the patient positioning is quantified by a three-dimensional translation and rotation of the patient. The parameter values are obtained by fitting the projections of anatomical match points in the simulator and the portal image, using the three-dimensional gantry coordinates of these points in the intended treatment setup. Two methods for finding an analytical solution for the fitting problem are presented. One method yields a solution from the affine transformation of the portal image (shift, rotation, magnification, and stretching in a specified direction) that fits the projections of the match points on the simulator image. The second and more accurate method yields a solution that expresses the estimated parameter values and their covariances as functions of image and three-dimensional gantry coordinates of the selected matchpoints. The robustness and sensitivity of the solution is implicitly given by these expressions. The applications of these methods are illustrated by experiments with a human pelvic bone.

Humans↗