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1

Automated geometric features evaluation method for normal foot skeleton model

Borucki B., Nowiński K., Chlebiej M., Rutkowski A., Adamczyk P., Laskowski J.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2011

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Vol. 11, no. 4

61--75

EN

Normal foot model is a geometric model of a healthy human foot. As the comparison of the processed feet requires a reference ideal healthy foot parameterization it was necessary to create such a model by defining skeleton geometric features and generating the feature set on a dataset population. Manual positioning of such number of landmarks is both a complex and time consuming task for a skilled radiologist, not to mention the total cost of such a procedure. Thus it was recommended to formulate an automated computer algorithm to perform this procedure with accuracy at a comparable level as the manual process. The following paper describes our approach based on automatic landmark positioning in a volumetric foot dataset. The proposed automated procedure is based on four main steps: manual landmark positioning on a reference dataset, registration of the reference dataset with the examined study, transformation of landmark positions from the reference dataset space into the examined dataset space, and calculation of the geometric features on the basis of landmarks positions. The results of our algorithm are presented and discussed in the context of pros and cons of the automated method itself as well as in the context of the generated normal foot model.

2

Image sequence stabilization through model based registration

Denkowski M., Chlebiej M.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2010

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Vol. 10, no. 1

79--86

EN

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.

3

Heart motion visualization tools for 4D echocadiographic images

Chlebiej M., Nowiński K., Ścisło P., Bała P.

Journal of Medical Informatics & Technologies

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2007

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Vol. 11

177--184

EN

Qualitative and quantitative description of the heart wall motion is a very important field of investigation in modern cardiology. Abnormalities in heart motion are usually symptoms of life threatening cardiac dysfunctions therefore measurements of dynamic heart functions are of great clinical importance. The images of moving spatial heart structures can be efficiently acquired using 4D echocardiography. Unfortunately because of the low quality such images do not allow for precise measurements. To overcome this problem images need to be further processed and moving structures have to be extracted. In this work we present a method for estimating heart motion from 3D echocardiographic image sequence. On the basis of this method we have developed several visualization techniques that enable qualitative assessments of heart motions abnormalities. Together with quantitative measurements they may be become a useful tools in daily clinical practice.

4

Registration approaches for the optimisation of medical diagnostics and treatment

Chlebiej M., Król Z., Mikołajczak P.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

57--64

EN

Many medical diagnostics and treatment procedures in the oncology, cranio-maxillofacial surgery, radiotherapy and neurosurgery deal with volumetric as well as surface data. Tumor detection or generation of the virtual treatment scene involves using complementary data sets that are obtained from different sensors, for example MR and CT data, or by the same sensor at different epochs. The very need for registration arises from the fact that the complementary data sets are acquired by imaging devices using different spatial coordinate systems or/and the anatomically correct superposition of two data instances cannot be performed without locally applied elastic transformation. The volume or surface matching is therefore an essential task in these applications. From the mathematical point of view the data aligning problem is an optimization task, which can be solved by using deterministic or non-deterministic optimization algorithms. Depending on the data size and the complexity of required matching transformation the runtime behaviour of the registration methods can stretch out between real-time and many hours' computations. In this paper various applications of the same registration paradigm are presented and discussed. The wide spectrum of the medical applications shows the importance of the registration approach for the optimization of medical diagnostics and treatment.

5

Registration and fuzzy segmentation modules for SemiVis framework

Denkowski M., Chlebiej M., Mikołajczak P.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

65--74

EN

This paper presents the cross-platform framework for image processing with a focus on medical imaging. It allows a fast addition and testing of new algorithms using a modular structure. New modules can be created by using a platform-independent The C++ class library can be easily integrated with a whole system by a plug-in mechanisms. Together with the system core in the framework medical image processing modules are included. The plug-in mechanism allows to create a processing pipelines of this modules to achieve sophisticated processing functions such as registration or segmentation.

6

Visible Human Projects - a step towards the virtual patient: models and simulations

Król Z., Chlebiej M., Mikołajczak P., Hoffmann K.-H.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

MIP51--58

EN

This paper reports on our experiences using datasets from the Visible Human Project in different biomedical applications. Introduced 1994 by the US National Library of Medicine the digitized multimodal anatomical datasets of the Visible Man have challenged the worldwide scientific community. A significant response to this challenge from several interdisciplinary research teams has emerged as a new area of research. This area requires close interaction and collaboration among anatomists, radiologists, computer scientists, mathematicians, engineers and physicians. The digitized volumetric images of the human body have been applied not only for the computer-aided exploration of the human gross anatomy, but also as structural input for the therapy planning and simulation systems. The importance of such virtual patient model is becoming increasingly recognized in modern medicine. To effectively use these specific datasets a sophisticated framework consisting of image processing, computer graphics and mathematical modelling methods is required. In this work various aspects of the developed framework are presented and discussed. Some preliminary results of our biomedical simulations are presented.

7

Segmentation and visualisation MR images of the human brain

Denkowski M., Chlebiej M., Mikołajczak P.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

MIP59--68

EN

Segmentation and visualisation of anatomical regions of the brain are fundamental problems in medical image analysis. In this paper, we present a fuzzy-logic segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The presented method consists of two main stages: histogram thresholding and pixel classification using a rule-based fuzzy logic inference. After the segmentation is complete, attributes of different tissue classes may be determined (e.g., volumes), or the classes may be visualised as spatial objects. The implemented system provides many advanced 3D imaging tools.

8

Numerical treatment of registration problem in generation of patient-specific anatomical models

Chlebiej M., Król Z., Mikołajczak P.

Journal of Medical Informatics & Technologies

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2003

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Vol. 6

IT13--20

EN

Registration is an important component of many medical data processing applications. Particularly significant is its role in the correlation of volumetric medical data aiming at generation of virtual patient-specific anatomical models. Such models enable optimization of various diagnostic and therapeutical procedures. The importance of the virtual patient models is becoming increasingly recognized in modern medicine. The advantages of using such biomedical virtual models are analogous to the advantages of real system behavior simulation in the engineering or material sciences. In this work some numerical issues associated with the registration problem and the visualization challenges arising in the context of virtual anatomical models have been presented and discussed.

9

Patient-specific graft design method for cranofacial surgical planning

Król Z., Chlebiej M., Zerfass P., Zeilhofer H.-F., Sader R., Mikołajczak P., Keeve E.

Journal of Medical Informatics & Technologies

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2002

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Vol. 3

MI189--197

EN

This paper presents a method for computer assisted selection of optimal donor sites for autologous osseous grafts in the craniofacial surgery. At the initial graft design stage the surgeon defines in the CT data set the shape of the bone segment to be reconstructed and in the donor region CT data set a set of constraints for the optimization task. This non-automatic step is followed by a fully automatic optimization stage, which delivers a set of sub-optimal and optimal donor sites for a given template. Such approach permits the surgeon to find the best site for harvesting the graft and enables an exact anatomical reconstruction of the osseous section.

10

Semi-automatic quantitative histogram analysis of the CT and MRI images

Chlebiej M., Kuczyński K., Stegierski R., Mikołajczak P.

Journal of Medical Informatics & Technologies

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2001

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Vol. 2, Part. 2

MT57--MT65

EN

This paper presents results on voxel histogram analysis for quantification of brain image sequence. We model the histogram as a sum of parameterized gaussian functions, where each function represents the distribution of samples for a single material in the volume. We find parameters for the collection of gaussian functions with the help of Levenberg-Marquardt method that make the model agrees with the histogram.