This paper presents a comprehensive metrological analysis of the Microsoft Kinect motion sensor performed using a proprietary flat marker. The designed marker was used to estimate its position in the external coordinate system associated with the sensor. The study includes calibration of the RGB and IR cameras, parameter identification and image registration. The metrological analysis is based on the data corrected for sensor optical distortions. From the metrological point of view, localization errors are related to the distance of an object from the sensor. Therefore, the rotation angles were determined and an accuracy assessment of the depth maps was performed. The analysis was carried out for the distances from the marker in the range of 0.8−1.65 m. The maximum average error was equal to 23 mm for the distance of 1.6 m.

Cardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on the recently introduced variant of the level set method called level set without edges. This variant takes advantage of the intensity value of area information instead of module of gradient which is typically used. Such approach guarantees stability and correctness of algorithm working on the border between object and background with small absolute value of image gradient. To reassure meaningful results, the image segmentation is proceeded with automatic Region of Interest (ROI) calculation. The main idea of ROI calculations is to receive a triangle-like part of the acquired ECHO image, using linear Hough transform, thresholding and simple mathematics. Additionally, in order to improve the images quality, an anisotropic diffusion filter, before ROI calculation, was used. The proposed method has been tested on real echocardiographic image sequences. Derived results confirm the effectiveness of the presented method.

With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

Minimally invasive procedures for the kidney tumour removal require a 3D visualization of topological relations between kidney, cancer, the pelvicalyceal system and the renal vascular tree. In this paper, a novel methodology of the pelvicalyceal system segmentation is presented. It consists of four following steps: ROI designation, automatic threshold calculation for binarization (approximation of the histogram image data with three exponential functions), automatic extraction of the pelvicalyceal system parts and segmentation by the Locally Adaptive Region Growing algorithm. The proposed method was applied successfully on the Computed Tomography database consisting of 48 kidneys both healthy and cancer affected. The quantitative evaluation (comparison to manual segmentation) and visual assessment proved its effectiveness. The Dice Coefficient of Similarity is equal to 0.871 ± 0.060 and the average Hausdorff distance 0.46 ± 0.36 mm. Additionally, to provide a reliable assessment of the proposed method, it was compared with three other methods. The proposed method is robust regardless of the image acquisition mode, spatial resolution and range of image values. The same framework may be applied to further medical applications beyond preoperative planning for partial nephrectomy enabling to visually assess and to measure the pelvicalyceal system by medical doctors.