Similarity analysis in medical image databases

The review of methods of similarity analysis of medical images is presented. Feature extraction, feature representation and different concepts of image query algebra problems are described and discussed from the medical application point of view. New algorithms based on medical image regularity description and intensity description are proposed. As a conclusion a Java application "ObrazMed" for content based medical image analysis is presented

Content-based retrieval system as a telemedical tool

The problem of medical teleconsultations with intelligent computer system rather than with a human expert is analyzed. System for content-based retrieval of images is described and presented as a use case of telemedical tool. Selected features, crucial for retrieval quality, are introduced including: synthesis of parametric images, regions of interest detection and extraction, definition of content-based features, generation of descriptors, query algebra, system architecture and performance. Additionally, electronic business pattern is proposed to generalize teleconsultation services like content-based retrieval system

Parametric imaging in dynamic glucose metabolism studies in brain

Parametric imaging is more and more popular in dynamic brain studies. It enables to quantitatively or semi-quantitatively estimate physiological state and processes in brain. This work analyse the dynamic 18FDG-PET studies for estimation of brain glucose metabolism. The influence of the signal noise is analysed to estimate its influence on the final glucose metabolism parameter values. The LCMRGlc parameter is under investigation. It is based on three compartmental model proposed by Phelps. Using different 18FDG-PET data series obtained from independent sources the Gaussian noise was introduced (with different variance). Then the quality of the model fitting results were estimated. The final results clearly indicates than the noise is highly compensated in microparameter used in calculation of LCMRGlc. Concluding, it is possible to estimate the LCMRGlc parameter value even in the presence of noise

Validity of MRI brain perfusion imaging method

Brain perfusion imaging using Dynamic Susceptibility Contrast Magnetic Resonance Imaging is very promising method since it can be easily implemented as a standard contrast-based MRI procedure. Quantitative brain perfusion description by DSC-MRI data post processing requires validation. Different validation analysis was performed to verify the influence of a bolus dispersion, delay, low SNR and calculation procedures on final perfusion parameter values. The results indicate that quantitative description of brain perfusion using DSC-MRI is possible and can be acceptable with accuracy about 10%

Integrated, radiological database structure

The rapid development of medical imaging requires the proper preparation of a medical database structure for the multimedia information acquired during a patient visit. This paper presents a study on the integrated structure of a radiological database based on the DICOM and HL7 standards and on medical coding systems. The conclusions are based on the Radiological Information System Project prepared for the Institute of Radiology at the Medical University of Gdansk

Registration and normalization of MRI/PET images

Parametric imaging is more and more popular in dynamic brain studies. It enables to quantitatively or semi-quantitatively estimate physiological state and processes in brain. Parametric images represent spatial distribution of parameter values calculated for chosen mathematical model of the process or object. This work compares different methods of geometrical transformations for image registration and normalization. Appropriate method for image registration and normalization (in reference to atlases) is extremely important for common visualization of structural and parametric images in MRI and PET studies. Rigid and elastic geometrical transformations are implemented and compared. Additionally Delaunay triangulation and image morphing methods are used. Manual and proposed automatic registration and normalization methods are presented and compared based on MRI/PET and Talairach atlas images. Concluding, the proposed automatic image normalization method is accurate and using the combination of Delaunay and morphing methods can produce even better results

Applications of image registration in parametric imaging

Applications of image registration in parametric imaging are investigated. The manual and automatic image registration methods have been used for image to image registration in sequences to correct movement artefacts in reconstructed parametric objects. Additionally the registration methods were used for multimodal visualisation of structural and parametric objects. The achieved results proved that the automatic image to image registration, for motion mechanisms correction, in the parametric model improves the quality of images. The multimodal visualisation of structural MRI images and parametric DSC-MRI images, enables to correlate the local dynamic changes with all morphological features

Big data significance in remote medical diagnostics based on deep learning techniques

In this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential challenges of using, storing and transferring sensitive patient data are discussed