Research goal: The aim of the research is to define the possibilities of TVU and the MRI in the diagnosis of the most common benign ovarian lesions which cause pelvic pain. Patients and methods: In study were included n=74 patients with pelvic pain, who were examined with TVU and then with an MRI of pelvis. Diagnostic results of all patients (n=74) divided into two groups according to the modality that was performed (TVU results n=74 and MRI results n=74 MRI ). We compared the results of TVU and MRI, and with a pathohistological finding after surgery. TVU test sensitivity and MRI test sensitivity has been made for each pathological entity in particular. The overall sensitivity test of TVU was performed for all pathological entities together. The overall sensitivity test of MRI was performed for all pathological entities together. Results: TVU demonstrated sensitivity of 83.3% for ectopic pregnancy, 83.3% for ovarian torsion, 84% for endometriotic cyst, 88.2% for hemorrhagic cysts, 58.3% for tubo-ovarian abscesses, 62.5% for dermoid cysts. Overall sensitivity of TVU for all these pathological entities was 78.4%. MRI showed a sensitivity of 100% for ovarian ectopic pregnancy, 83.3% for ovarian torsion, 100% for endometriotic cyst, 100% of hemorrhagic cysts, 83.3% tubo-ovarian abscess, and 87.5% for dermoid cysts. Overall sensitivity of MRI in all of these pathological entities was 94.6%. The analysis using the chi square test shows that there is a significant difference in the sensitivity between the US and MRI in favor of greater overall MRI sensitivity in diagnosing ovarian pain caused by benign lesions. (χ2 = 14.352, df = 9, p = 0.0021). Conclusion: TVU is the first choice method for ovarian analysis due to the convenience and absence of radiation, and MRI is a very useful modality when TVU’s results are confusing and unspecific.

Abstract This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment.

Abstract This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework.