Background: The objective of this study was to verify the effect that plot locational errors, termed plot co-registration errors, have on the strength of regression between LiDAR canopy metrics and the measured total standing volume (TSV) of plots in a Pinus radiata forest. Methods: A 737 hectare plantation of mature Pinus radiata located in Northern Hawkes Bay was selected for the study. This forest had been measured in a pre-harvest inventory and had aerial LiDAR assessment. The location of plots was verified using a survey-grade GPS. Least square linear regression models were developed to predict TSV from LiDAR canopy metrics for a sample of 204 plots. The regression strength, accuracy and bias was compared for models developed using either the actual (verified) or the incorrect (intended) locations for these plots. The change to the LiDAR canopy metrics after the plot co-registration errors was also established. Results: The plot co-registration error in the sample ranged from 0.7 m to 70.3 m, with an average linear spatial error of 10.6 m. The plot co-registration errors substantially reduced the strength of regression between LiDAR canopy metrics and TSV, as the model developed from the actual plot locations had an R2 of 44%, while the model developed from the incorrect plot locations had an R2 of 19%. The greatest reductions in model strength occurred when there was less than a 60% overlap between the plots defined by correct and incorrect locations. Higher plot co-registration errors also caused significant changes to the height and density LiDAR canopy metrics that were used in the regression models. The lower percentile elevation LiDAR metrics were more sensitive to plot co- registration errors, compared to higher percentile metrics. Conclusion: Plot co-registration errors have a significant effect on the strength of regressions formed between TSV and LiDAR canopy metrics. This indicates that accurate measurements of plot locations are necessary to fully utilise LiDAR for inventory purposes in forests of Pinus radiata.

Clinically it is important to combine information provided by mammographic images from multiple views or at different times. Taking regular mammographic screening and comparing corresponding mammograms are necessary for early detection of breast cancer, which is the key to successful treatment. However, mammograms taken at different times are often obtained under different compressions, orientations or body positions. A temporal pair of mammograms may vary quite significantly due to the spatial disparities caused by the variety in acquisition environments, including the 3D position of the breast, the amount of the pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We have implemented and utilized finite element models for temporal registration of digital mammography. In our work, we applied the patient specific breast model, where patients have both mammograms and MRIs available, and generic model, where only patient mammograms are available. After we applied the temporal registration algorithm, the average error among the 14 patient datasets was 3.4 plus/minus 0.86 mm for Euclidean distance and 4.3 plus/minus 0.52 mm for predicted 2D lesion position. With generic model, the average error among the 14 patient datasets using the measure of Euclidean distance between the predicted lesion position in T1 and T2 was 5.0 plus/minus 0.74 mm for Euclidean distance and 5.7 plus/minus 0.83 mm for predicted 2D lesion position. Compared with the average lesion size (10mm~40mm), this error is acceptable. With lesion correspondence, our finite element method can be used to suppress technical variations (e.g., mammogram positioning or compression) and to emphasize genuine alterations in the breast.

Not available / text

Not available / text

Not available / text

published_or_final_version / Public Administration / Master / Master of Public Administration

In this work were studied, implemented and evaluated two algorithms of image registration with two similarity metrics of mutual information. These were Viola-Wells Mutual Information [6],[7] and Mattes Mutual Information[11]. Materials and Methods: Two 3D MRI T1 and Τ2 brain images were used. The T1 image was rotated in all three axes , with the 27 possible triples of angles 0.25, 1.5 and 3 degrees and in the T2 image were added 3 Gaussian Noise Levels (1,3,5%). Thus were formed two experiments. The monomodal experiment which was registering the initial T1 image with its 27 rotated instances and the multimodal experiment which was registering the 4 T2 images (0,1,3,5% Gaussian Noise) with the 27 rotated T1 images. The registration framework had also a Regular Step Gradient Descent Optimizer, affine linear transformation and linear interpolator. After the 5 experimental set were registered with both algorithms, then in order for the results to be evaluated, 5 similarity metrics were used. These were: 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Finally t-test was applied, in order to find statistically significant differences. Results: Both algorithms had similar outcome, although the algorithm with Mattes Μutual Information metric, had a slightly improved performance. Statistically important differences were found in the t-test. Conclusions: The two methods should be tested more, using other kinds of transformation, and more data sets. / Σε αυτήν την εργασία μελετήθηκαν, υλοποιήθηκαν και αξιολογήθηκαν δύο αλγόριθμοι αντιστοίχισης ιατρικών εικόνων με δύο μετρικές ομοιότητας με χρήση κοινού πληροφορίας. Συγκεκριμένα η υλοποίηση Viola-Wells [6],[7] και η υλοποίηση Mattes[11]. Υλικά και Μέθοδος: Χρησιμοποιήθηκαν δύο εικόνες 3D MRI T1 και Τ2 που απεικόνιζαν εγκέφαλου. Η εικόνα Τ1 περιστράφηκε με τους 27 δυνατές συνδυασμούς των γωνιών 0.25,1.5,3 μοιρών , σε όλους τους άξονες και στην εικόνα Τ2 προστέθηκαν 3 επίπεδα Gaussian θορύβου (1,3,5%). Έτσι σχηματίστηκαν δύο πειράματα. Το μονο-απεικονιστικό πείραμα (Monomodal) που αντιστοιχούσε την αρχική Τ1 εικόνα με τα 27 περιστρεμμένα στιγμιότυπα της και το πολύ-απεικονιστικό (multimodal) που αντιστοιχούσε τις 4 Τ2 εικόνες (0,1,3,5% Gaussian Noise) με τα 27 περιστρεμμένα στιγμιότυπα της Τ1. Το σχήμα της αντιστοίχισης αποτελούνταν εκτός από τις δύο μετρικές ομοιότητας, από τον Regular Step Gradient Descent βελτιστοποιητή , συσχετισμένο (affine) γραμμικό μετασχηματισμό και γραμμικό interpolator. Αφού τα 5 σύνολα πειραμάτων ταυτίστηκαν και με τους 2 αλγορίθμους στην συνέχεια και προκειμένου να αξιολογηθεί το αποτέλεσμα της αντιστοίχισης, χρησιμοποιήθηκαν 5 μετρικές ομοιότητας. Αυτές ήταν : 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Τέλος εφαρμόστηκε και t-test προκειμένου να επιβεβαιωθούν στατιστικώς σημαντικές διαφορές. Αποτελέσματα: Και οι δύο αλγόριθμοι βρέθηκαν να έχουν παρόμοια συμπεριφορά, ωστόσο ο αλγόριθμος που χρησιμοποιούσε την Mattes Μutual Information μετρική ομοιότητας είχε καλύτερα αποτελέσματα. Στατιστικώς σημαντικές διαφορές επιβεβαιώθηκαν και από το t-test. Συμπέρασμα: Οι δύο μέθοδοι θα πρέπει να αξιολογηθούν χρησιμοποιώντας και άλλους μετασχηματισμούς, καθώς και διαφορετικά data set.

Confocal microendoscopy is a promising new diagnostic imaging technique that is minimally invasive and provides in-vivo cellular-level images of tissue. In this study, we developed various image analysis techniques for ovarian cancer detection using the confocal microendoscope system. Firstly, we developed a technique for automatic classification of images based on focus, to prune out the out-of-focus images from the ovarian dataset. Secondly, we modified the texture analysis technique developed earlier to improve the stability of the textural features. The modified technique gives stable features and more consistent performance for ovarian cancer detection. Although confocal microendoscopy provides cellular-level resolution, it is limited by a small field of view. We present a fast technique for stitching the individual frames of the tissue to form a large mosaic. Such a mosaic will aid the physician in diagnosis, and also makes quantitative and statistical analysis possible on a larger field of view.

Sub-pixel image alignment estimation is desirable for co-registration of objects in multiple images to a common spatial reference and as alignment input to multi-image processing. Applications include super-resolution, image fusion, change detection, object tracking, object recognition, video motion tracking, and forensics.Information theoretical measures are commonly used for co-registration in medical imaging. The published methods apply Shannon's Entropy to the Joint Measurement Space (JMS) of two images. This work introduces into the same context a new set of statistical divergence measures derived from Fisher Information. The new methods described in this work are applicable to uncorrelated imagery and imagery that becomes statistically least dependent upon co-alignment. Both characteristics occur with multi-modal imagery and cause cross-correlation methods, as well as maximum dependence indicators, to fail. Fisher Information-based estimators, together as a set with an Entropic estimator, provide substantially independent information about alignment. This increases the statistical degrees of freedom, allowing for precision improvement and for reduced estimator failure rates compared to Entropic estimator performance alone.The new Fisher Information methods are tested for performance on real remotely-sensed imagery that includes Landsat TM multispectral imagery and ESR SAR imagery, as well as randomly generated synthetic imagery. On real imagery, the co-registration cost function is qualitatively examined for features that reveal the correct point of alignment. The alignment estimates agree with manual alignment to within manual alignment precision. Alignment truth in synthetic imagery is used to quantitatively evaluate co-registration accuracy. The results from the new Fisher Information-based algorithms are compared to Entropy-based Mutual Information and correlation methods revealing equal or superior precision and lower failure rate at signal-to-noise ratios below one.

High-precision optical systems are generally tested using interferometry, since it often is the only way to achieve the desired measurement precision and accuracy. Interferometers can generally measure a surface to an accuracy of one hundredth of a wave. In order to achieve an accuracy to the next order of magnitude, one thousandth of a wave, each error source in the measurement must be characterized and calibrated.Errors in interferometric measurements are classified into random errors and systematic errors. An approach to estimate random errors in the measurement is provided, based on the variation in the data. Systematic errors, such as retrace error, imaging distortion, and error due to diffraction effects, are also studied in this dissertation. Methods to estimate the first order geometric error and errors due to diffraction effects are presented.Interferometer phase modulation transfer function (MTF) is another intrinsic error. The phase MTF of an infrared interferometer is measured with a phase Siemens star, and a Wiener filter is designed to recover the middle spatial frequency information.Map registration is required when there are two maps tested in different systems and one of these two maps needs to be subtracted from the other. Incorrect mapping causes wavefront errors. A smoothing filter method is presented which can reduce the sensitivity to registration error and improve the overall measurement accuracy.Interferometric optical testing with computer-generated holograms (CGH) is widely used for measuring aspheric surfaces. The accuracy of the drawn pattern on a hologram decides the accuracy of the measurement. Uncertainties in the CGH manufacturing process introduce errors in holograms and then the generated wavefront. An optimal design of the CGH is provided which can reduce the sensitivity to fabrication errors and give good diffraction efficiency for both chrome-on-glass and phase etched CGHs.