Comparative analysis of filtering methods in fuzzy C-means: environment for DICOM image segmentation

Medical image analysis was done using a sequential application of low-level pixel processing and mathematical modeling to develop rule-based systems. During the same period, artificial intelligence was developed in analogy systems. In the 1980s magnetic resonance or computed tomography imaging system has been introduced that encode and decode the output of the images. Digital imaging and communications in medicine (DICOM) has improved the communication mechanism in the medical environment. In products such as CT, MR, X-ray, NM, RT, US, etc., DICOM is used for image storing, printing the information about the patient’s condition, and transmitting the correct information about the radiological images. It involves a file format and protocol in communication networks. It is useful for receiving images and patient data in DICOM format. DICOM format has been widely adopted to all medical environments and derivations from the DICOM standard are used into other application areas. DICOM is the basis of digital imaging and communication in nondestructive testing and in security. DICOM data consist of many attributes including information such as name, ID, and image pixel data. A single DICOM object can have only one attribute containing pixel data. Pixel data can be compressed using a variety of standards, including JPEG, JPEG Lossless, JPEG 2000, and Run-length encoding

Wavelet analysis and enhancement of MR tomography and ultrasound images

Tomografické MR (Magnetic Resonance) a ultrazvukové zpracování biosignálů jsou důležité neinvazivní diagnostické metody v lékařství. Vstupní zesilovač tomografu a obvody ultrazvuku vnáší do zpracování šum, který dosti znehodnocuje diagnostiku daného orgánu. Obrazová data jsou ukládána ve standardizovaném lékařském formátu DICOM. V této práci byly navrženy metody využívající waveletové analýzy k potlačení šumu v obrazech a bylo provedeno jejich srovnání s klasickými metodami. K zpracování dat i jejich zápisu zpět do DICOM formátu bylo využito programu MATLAB.Tomographic MR (Magnetic Resonance) and sonographic biosignal processing are important non-invasive diagnostic methods used in a medicine. A noise added into processed data by an amplifier of tomograph receiving part and by circuits of sonograph is resulting in a body organ diagnosis degradation. Image data are stored in a standardized DICOM medical file format. Methods using wavelet analysis for noise suppression in image data have been designed and their comparation with classical methods has been made in this work. The MATLAB was utilized for data processing and data rewriting back to the DICOM format.

On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi

Computational medical imaging for total knee arthroplasty using visualitzation toolkit

This project is presented as a Master Thesis in the field of Civil Engineering, Biomedical specialization. As the project of an Erasmus exchange student, this thesis has been under supervision both the Universite Livre de Bruxelles and the Universitat Politecnica de Catalunya. The purpose of this thesis to put in practice all the knowledges acquired during this Master in Industrial Engineering in UPC and to be a support for medical staff in total knee arthoplasty procedures. Prof. Emmanuel Thienpont has been working for years as orthopaedic surgeon at the Hospital Sant Luc, Brussels. His years of work and research have been mainly focused on Total Knee Arthroplasty or TKA. During one of the most important steps of this procedure, the orthopaedic surgeon has to cut the head of the femur following two perpendicular cutting planes. Nevertheless, the orientation of these planes are directly dependant of the femur constitution. This Master Thesis has been conceived in order to offer the surgeon a tool to determine the proper direction planes in a previous step before the surgical procedure. This project pretends to give the surgeon an openfree computational platform to access to patient geometrical and physiological information before involving the subject in any invasive procedure

Research Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain

Medical Image analysis and processing has great significance in the field of medicine, especially in Non- invasive treatment and clinical study. Medical imaging techniques and analysis tools enable the Doctors and Radiologists to arrive at a specific diagnosis. Medical Image Processing has emerged as one of the most important tools to identify as well as diagnose various disorders. Imaging helps the Doctors to visualize and analyze the image for understanding of abnormalities in internal structures. The medical images data obtained from Bio-medical Devices which use imaging techniques like Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Mammogram, which indicates the presence or absence of the lesion along with the patient history, is an important factor in the diagnosis. The algorithm proposes the use of Digital Image processing tools for the identification of Hemorrhage and Infarct in the human brain, by using a semi-automatic seeded region growing algorithm for the processing of the clinical images. The algorithm has been extended to the Real-Time Data of CT brain images and uses an intensity-based growing technique to identify the infarct and hemorrhage affected area, of the brain. The objective of this paper is to propose a seeded region growing algorithm to assist the Radiologists in identifying the Hemorrhage and Infarct in the human brain and to arrive at a decision faster and accurate.¢Lp¤

3D Reconstruction of CT Scans For Visualization in Virtual Reality

Computed tomography allows analyzing the internal structure of an object, which is useful especially in medicine. The standard visualization displays scans in the 2D plane. 3D reconstruction of scans provides a complex image of the morphology of the scanned object. Matlab is a software commonly used for image processing and analysis. It includes Medical Image Processing Toolbox for displaying data from CT scan in DICOM format. However, it is not possible with this toolbox to export the dataset of the image as a 3D object. Therefore, the aim of the paper is the implementation of a toolbox for loading and displaying data as a 3D reconstruction. This toolbox allows the user to export the data in OBJ or STL format. That allows the user (i) to visualize the 3D models in virtual reality and (ii) to prepare the model for 3D printing. The OBJ model is imported to Blender and then exported out with a texture as an object file. In Unity, we created a 3D scene and imported model. The advantage of displaying the 3D model in virtual reality is a more realistic view of the shape and dimension of an object.Výpočetní tomografie umožňuje studovat vnitřní strukturu objektu, což je využíváno především v medicíně. Standardní zobrazovací techniky promítají snímky ve 2D rovině. 3D rekonstrukce snímků přináší komplexní pohled na morfologii snímané tkáně. Matlab je software běžně užívaný v oblasti zpracování a analýze obrazových dat. Zároveň obsahuje nástroj “Image Procesessing Toolbox”, který umožňuje zobrazit CT snímky uchované ve formátu DICOM. Tento nástroj však neumožňuje vyexportovat zobrazený model jako 3D objekt. Cílem tohoto projektu bylo vytvoření nástroje pro načítání a zobrazení zrekonstruovaných 3D modelů. Tento nástroj umožňuje uživateli vyexportovat data v OBJ nebo STL formátu, který umožňuje (i) vizualizovat 3D model ve virtuální realitě a (ii) připravit model vhodný pro 3D tisk. V editor Unity byla vytvořena 3D scéna a do ní byl importován vygenerovaný model. Výhodou zobrazení 3D modelu ve virtuální realitě je přirozený pohled na prostorové uspořádání objektu

Comparison of the accuracy of voxel based registration and surface based registration for 3D assessment of surgical change following orthognathic surgery

Purpose: Superimposition of two dimensional preoperative and postoperative facial images, including radiographs and photographs, are used to evaluate the surgical changes after orthognathic surgery. Recently, three dimensional (3D) imaging has been introduced allowing more accurate analysis of surgical changes. Surface based registration and voxel based registration are commonly used methods for 3D superimposition. The aim of this study was to evaluate and compare the accuracy of the two methods.<p></p> Materials and methods: Pre-operative and 6 months post-operative cone beam CT scan (CBCT) images of 31 patients were randomly selected from the orthognathic patient database at the Dental Hospital and School, University of Glasgow, UK. Voxel based registration was performed on the DICOM images (Digital Imaging Communication in Medicine) using Maxilim software (Medicim-Medical Image Computing, Belgium). Surface based registration was performed on the soft and hard tissue 3D models using VRMesh (VirtualGrid, Bellevue City, WA). The accuracy of the superimposition was evaluated by measuring the mean value of the absolute distance between the two 3D image surfaces. The results were statistically analysed using a paired Student t-test, ANOVA with post-hoc Duncan test, a one sample t-test and Pearson correlation coefficient test.<p></p> Results: The results showed no significant statistical difference between the two superimposition methods (p<0.05). However surface based registration showed a high variability in the mean distances between the corresponding surfaces compared to voxel based registration, especially for soft tissue. Within each method there was a significant difference between superimposition of the soft and hard tissue models.<p></p> Conclusions: There were no significant statistical differences between the two registration methods and it was unlikely to have any clinical significance. Voxel based registration was associated with less variability. Registering on the soft tissue in isolation from the hard tissue may not be a true reflection of the surgical change