Marcin Rudzki

Role of radiologists in CAD life-cycle

A modern CAD (computer-aided diagnosis) system development involves a multidisciplinary team whose members are experts in medical and technical fields. This study indicates the activities of medical experts at various stages of the CAD design, testing, and implementation. Those stages include a medical analysis of the diagnostic problem, data collection, image analysis, evaluation, and clinical verification. At each stage the physicians knowledge and experience are indispensable. The final implementation involves integration with the existing Picture Archiving and Communication System. The term CAD life-cycle describes an overall process of the design, testing, and implementation of a system that in its final form assists the radiologists in their daily clinical routine. Four CAD systems (applied to the bone age assessment, Multiple Sclerosis detection, lung nodule detection, and pneumothorax measurement) developed in our laboratory are given as examples of how consecutive stages are developed by the multidisciplinary team. Specific advantages of the CAD implementation that include the daily clinical routine as well as research and education activities are discussed.

Activity Monitoring of the Elderly for Telecare Systems - Review

Aging society in developed countries forms new challenges for medical care. Ensuring well-being of the elderly and provide them support when required is of special concern. Nowadays, solutions in form of telecare centers and monitoring systems are able to estimate condition of the patient and provide necessary help accordingly. This paper addresses the problem of reviewing current state of the art by describing systems available commercially and presented in scientific papers as well as inference methods for condition assessment. Conclusions and problems to be taken into account while designing such systems are also pointed out.

Wearable System for Activity Monitoring of the Elderly

The paper presents a concept of an Ambient Assisted Living system for daily monitoring of the elderly. Description and testing of the prototype of a mobile data acquisition device for physical activity detection using inertial sensors is made. Testing has been performed on a mechanical test stand and a group of healthy individuals during normal walking. The purpose has been to assess the repeatability of acquired data. Obtained results are satisfactory and further development takes place.

Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis

In this paper a novel multi-stage automatic method for brain tumour detection and neovasculature assessment is presented. First, the brain symmetry is exploited to register the magnetic resonance (MR) series analysed. Then, the intracranial structures are found and the region of interest (ROI) is constrained within them to tumour and peritumoural areas using the Fluid Light Attenuation Inversion Recovery (FLAIR) series. Next, the contrast-enhanced lesions are detected on the basis of T1-weighted (T1W) differential images before and after contrast medium administration. Finally, their vascularisation is assessed based on the Regional Cerebral Blood Volume (RCBV) perfusion maps. The relative RCBV (rRCBV) map is calculated in relation to a healthy white matter, also found automatically, and visualised on the analysed series. Three main types of brain tumours, i.e. HG gliomas, metastases and meningiomas have been subjected to the analysis. The results of contrast enhanced lesions detection have been compared with manual delineations performed independently by two experts, yielding 64.84% sensitivity, 99.89% specificity and 71.83% Dice Similarity Coefficient (DSC) for twenty analysed studies of subjects with brain tumours diagnosed.

Artificial neural networks in accelerometer-based human activity recognition

This paper presents a study aimed to assess applicability of artificial neural networks (ANNs) in human activity recognition from simple features derived from accelerometric signals. Secondary goal was to select the most descriptive signal features and sensor locations to be used as inputs to ANNs. Five triaxial accelerometers were attached to human body in the following places: one at back, two at waist laterally and two at both ankles. The set of activities to be recognized was established to include the most often performed actions in home environment. In total 25 subjects performed a set of predefined actions like walking, going up and down the stairs, sitting down and standing up from a chair. Acquired signals were divided into 0.5s time windows by a label defining the action performed. Several statistical signal features were calculated and used to train ANNs. Learning and testing were performed on separate data sets. Analysis using Fisher Linear Discriminant showed that despite the fact that some of the calculated values play a significant role in the distinction between similar activities, none of the features or sensors could be omitted in the recognition of the activities considered in the study. Accuracy of 97% has been achieved for discriminating sitting and walking, 89% for standing, 72-75% for walking the stairs. Transient actions like standing up and sitting down have been detected with accuracy 56% and 38%, respectively. Even though there are studies declaring higher accuracy, none of them considered a set of activities analyzed in this research.

A new fuzzy support vectors machine for biomedical data classification

In this paper a new approach to a fuzzy support vector machine (FSVM) for solving multi-class problems is presented. The developed algorithm combines two separate methods based on fuzzy support vector machine, one for solving two-class problems and the second for multi-class problems. The first method deals with the problem of selecting the best support vector machine (SVM) kernel function and the second method enables classification of unclassified regions that appear when classical SVM methods for solving multi-class problems are used. Presented tool has been subjected to the dataset from Kent Ridge Biomedical Data Set Repository and showed its superiority comparing with conventional SVM and FSVM methods.

Quantitative Validation of Gait and Swing Angles Determination from Inertial Signals

The still increasing life length expectancy creates new challenges in the field of senior care. It encourages researchers to provide the nursing homes and senior care assistants with tools that will both, rise an alarm in case of a sudden fall and collect data for long-term diagnosis of the declining motor abilities like the number of steps taken per day or changes in some gait parameters. This paper presents a quantitative validation of a remote system for activity monitoring of the elderly based on inertial sensors. It focuses on features connected to walk quality such as number of steps and the swing angle outlined by an ankle in the sagittal plane during walk. A measurement protocol is proposed, a validation method is described and the obtained results are discussed.

An image processing approach to determination of steel fibers orientation in reinforced concrete

The paper presents a preliminary study aimed to automatically determine the position and orientation of steel fibers in fiber-reinforced concrete. This is required for assessment of the relation between the methods of forming and resulting concentration, position and orientation of steel fibers. Concrete beams with various types of fibers and method of forming were scanned using Computed Tomography and the resulting volumetric images were subjected to image segmentation. From the obtained label map the position and orientation in 3D of each steel fiber were calculated. This enabled generating 4D histograms visualizing in compact form the overall orientation of the fibers. Statistical analysis showed that the orientation of the fibers exhibit exponential distribution.

Computer aided diagnosis workstation for brain tumor assessment

The paper presents a Computer Aided Diagnosis (CAD) software for brain tumor detection and analysis from Magnetic Resonance Imaging (MRI). The software utilizes a novel multi-stage method that is capable of dealing with three main types of brain tumors, i.e. HG gliomas, metastases and meningiomas and yields object masks as well as quantitative parameters. The processing method makes use of several available in MRI series: FLAIR, T1-Weighted, Contrast Enhanced T1-Weighted and Perfusion Weighted Images. Image processing methods involve registration of MR series, Region of Interest determination, multi-stage image segmentation, and finally analysis of neovasculature in suspected areas basing on perfusion maps. The brain regions are then labeled as normal, tumor or peritumoral. Relative perfusion coefficient is calculated with respect to a healthy white matter of the brain. Obtained results are presented to the radiologist. The presented CAD workstation is able to communicate with PACS (Picture Archiving and Communication System) using DICOM protocol.

Development of a Multimodal Image Registration and Fusion Technique for Visualising and Monitoring Chronic Skin Wounds

Chronic skin wounds from diabetes, atherosclerosis, and cancer form a large source of morbidity and medical complications. While individual imaging modalities (e.g. visual images, thermograms, ultrasound) can be useful for monitoring the healing process, their use is limited because of the difficulty acquiring and registering multiple images from different modalities. This paper presents a methodology for image registration using an alignment phantom for grayscale images and thermograms. The registration system achieves a Fiducial Registration Error of 0.61 mm and Mutual Information value of 0.774. Future studies will seek to add additional imaging modalities and improve registration for other areas of the body.