Effective Filtration Techniques for Gallbladder Ultrasound Images with Variable Contrast

This paper presents a new method of filtering gallbladder contours on static ultrasound images. A major stage in the analysis of ultrasound images is to segment and section off areas occupied by the said organ. In the majority of cases this procedure is a key phase in the process of diagnosing pathological changes in tested organs. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This also applies to the analysis of gallbladder images, chiefly targeted at recognizing changes, which may reveal evidence of developing inflammatory or cancerous changes. This paper provides for an inventive algorithm for the extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The proposed approach of gallbladder image segmentation allows to obtain the effective results of contour detection which exceed 70%. This method is based on two procedures, which independently determine the relevant contour points for both correctly contrasted and vague ultrasound images. The independent results obtained from both procedures when merged allowed for a final determination of a tested organ’s actual shape. Defined in such way morphology makes possible to perform a further classification, which is intended to define certain morbid changes such as inflammation, and cancer. The presented approach based on dynamically adjusted contrast improvement, expand traditional methods of ultrasound images segmentation by additional parallel filtration procedures. As shown in the paper such algorithm is easy scalable and effective.     

Mapping sequences active in homologous RNA recombination in brome mosaic virus: prediction of recombination hot spots

The mechanism of homologous recombination has been studied previously in brome mosaic virus (BMV), a tricomponent, positive-stranded RNA virus of plants, by using artificial sequences (reviewed by J. J. BujarskiP. D. Nagy (1996). Semin. Virol. 7, 363-372). Here we extend these studies over BMV-derived sequences to obtain clues on prediction of homologous recombination hot spots. First, mismatch mutations, which reduced the AU content, were introduced into the common 60-nt recombination hot-spot sequence, either in the RNA2 or in both RNA2RNA3 components. This decreased the frequency of targeted homologous RNA2/RNA3 recombinationchanged the distribution of junction sites. Second, several short BMV RNA1- or RNA2-derived sequences were introduced into the RNA3 component, homologous recombination activity of these sequences was compared with that observed for previously characterized artificial sequences. Third, sequences at homologous recombinant junctions were compared among a large number of targetednontargeted recombinants. All these studies revealed several factors important for homologous recombination including the length of sequence identity, the extent of sequence identity, the AU content of the common sequences, the relative position of the AU-rich segment vs a GC-rich segment,the presence of GC-rich sequences. Based on this novel model, we suggest that recombination hot spots can be predicted by means of RNA sequence analysis. In addition, we show that recombination can occur between positivenegative strands of BMV RNAs. This provides further clues toward the mechanism of recombination processes in BMV.     

Averaging of motion capture recordings for movements’ templates generation

In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates’ preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave - one - out cross - validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.