Comparison and evaluation of registration methods of intra-oral radiographs images

This paper presents comparisons and evaluations of registration methods of intra-oralradiograph images. Several automatic and manual algorithms were examined. Three similarityfunctions for automatic registration are described and evaluated. In addition, the results of twomanual registration tests are compared for both 3 and 10 control points marked interactively by theoperator

Image Sequence Stabilization Through Model Based Registration

Acquisition of image series using the digital camera gives a possibility to obtain high resolution/quality animation, much better than while using the digital camcorder. However, there are several problems to deal with when producing animation using such approach. Especially, if motion involves changes in observer position and spatial orientation, the resulting animation may turn out to look choppy and unsmooth. If there is no possibility to provide some hardware based stabilization of the camera during the motion, it is necessary to develop some image processing methods to obtain smooth animation. In this work we deal with the image sequence acquired without stabilization around an object. We propose a method that enables creation of smooth animation using the registration paradigm

A wide depth of field reconstruction method based on partially focused image series

The main problem with images acquired using macro photography is the very shallow depth-of-field. In this article we present and implement an algorithm to reconstruct full focused images based on partially focused images series of the same object acquired with different focus depths. The presented algorithm consists of several phases including: image registration, depth map creation, image reconstruction and final histogram and quality correction

Method for filling and sharpening false colour layers of dual energy X-ray images

An X-ray scanning and image processing have a vastrange of applications in the security. An image of a content ofsome package being passed for example to an airplane or to thecourt house may help to figure out if there are any dangerousobjects inside that package and to avoid possible threateningsituation. As the raw X-ray images are not always easy to analyzeand interpret, some image processing methods like an objectdetection, a frequency resolution increase or a pseudocolouringare being used. In this paper, we propose a pseudocoloringimprovement over material based approach. By addition of theedge detection methods we fill and sharpen colour layers overthe image, making it easier to interpret. We demonstrate theeffectiveness of the methods using real data, acquired from aprofessional dual energy X-ray scanner

Development of the cross-platform framework for the medical image processing

This paper presents the development process of a platform for image processing with a focus on the medical imaging. Besides general image processing algorithms and visualization tools, this platform includes advanced medical imaging modules for segmentation, registration and morphological analysis. It allows fast addition and testing of new algorithms using a modular structure. New modules can be created by using a platform-independent C++ class library and can be easily integrated with a whole system by a plug-in mechanism. An abstract, hierarchical definition language allows the design of efficient graphical user interfaces, hiding the complexity of the underlying module network to the end user

Histogram analysis of the human brain MR images based on the S-function membership and Shannon's entropy function

The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes segmentation as a preliminary stage for the visualization or quantification. In this paper, we present the first step in our fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The histogram analysis based on the S-function membership and the Shannon's entropy function provides finding exact segmentation points. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. When the segmentation is complete, attributes of these classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. In contrast to other segmentation methods, like thresholding and region-based algorithms, our methods proceeds automatically and allow more exact delineation of the anatomical structures

The paranasal sinusitis in the CT and MRI and in the CT/MR digital fusion images

Background: The aim of the study is to evaluate the CT and MR visualization of the sinusitis. The second focus is to investigate usefulness of digital fusion of these examinations. Material/Methods: 28 patients with suspicion of the sinusitis underwent the CT and MR exams. Finally the CT/MR digital fusion of all the examinations, using the own program, was performed. Evaluation of the quality of imaging the bones, soft tissues and mucosa was applied in all the techniques. Results: Both the modalities well depicted the soft and mucosal elements with a slight superiority of MR in imaging the discrete mucosal thickening. The small bones were better presented in CT. The fused images correctly depicted even the discrete mucosal changes on the background of small bony structures. Conclusions: CT better presented bone elements of sinuses; MR is slightly superior in imaging the mucosal thickening. Their digital fusion unified the advantages of both

Taking MT evaluation metrics to extremes : beyond correlation with human judgments

Automatic Machine Translation (MT) evaluation is an active field of research, with a handful of new metrics devised every year. Evaluation metrics are generally benchmarked against manual assessment of translation quality, with performance measured in terms of overall correlation with human scores. Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments. However, little insight has been provided regarding the weaknesses and strengths of existing approaches and their behavior in different settings. In this work we conduct a broad meta-evaluation study of the performance of a wide range of evaluation metrics focusing on three major aspects. First, we analyze the performance of the metrics when faced with different levels of translation quality, proposing a local dependency measure as an alternative to the standard, global correlation coefficient. We show that metric performance varies significantly across different levels of MT quality: Metrics perform poorly when faced with low-quality translations and are not able to capture nuanced quality distinctions. Interestingly, we show that evaluating low-quality translations is also more challenging for humans. Second, we show that metrics are more reliable when evaluating neural MT than the traditional statistical MT systems. Finally, we show that the difference in the evaluation accuracy for different metrics is maintained even if the gold standard scores are based on different criteria