mohammad reza ahmadzadeh

Puppet shows are a popular form of television. From these programs, popular puppet characters have been created that have remained in the minds of the viewers, and the audience of many children and adults has become interested in some of these puppets. Among them are the Kolah Ghermezi puppets and Jenab Khan. How to create this popularity and the factors affecting it is the question on which the present study is based. The purpose of this study is to examine the various factors that create popularity for television puppets among the public and viewers, the extent and priority of these factors in the eyes of the audience to be studied and analyzed. The method of this research consists of two stages. First, a survey of the audience about the reasons for the attractiveness of the puppets, and then a semi-structured interview with experts in this field, in order to examine the causes and motivations in popular opinion. Therefore, in order to achieve the desired goals, considering the continuity of production, distribution and communication with the audience in recent years, two Kolah Ghermezi and Jenab Khan were selected as the study sample. The data show that various factors, from technical and artistic features to the production team and broadcasting conditions, affect the popularity of TV puppets, but it is understood that most people preferred the content of the puppet show to its shape.so the importance people place on voice acting and storytelling in puppet shows is important to puppet show creators.

Lung’s air volume estimation is of great importance in lung disease diagnosis. In this paper a fully automatic algorithm, which we presented recently to estimate the lung’s air volume from CT-images, is more developed. In this algorithm, first a suitable cost function is introduced based on the long parenchyma physics to determine the voxels of lung’s air region. In this paper, a fully automatic framework is proposed to calculate the initial guess for the solution of the optimization problem. Moreover, a 3D model reconstruction technique is utilized to determine spatial localization of the lung’s air region in 3D CT-images. Furthermore, the performance of the whole-lung-volume-based methods and direct lung’s air volume measurment methods are compared and investigated. In order to evaluate the accuracy, porcine’s lung images and clinical human’s lung images from reliable databases are fed to the proposed algorithm. The significant accuracy and robust performance of the proposed algorithm is illustrated with respect to the resolution reduction of CT-images.

Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness‑based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non‑vessel structures. Experimental results for synthetic images and coronary X‑ray angiograms show desirable performance of our model.

Motion analysis and quality assessment of human sperm cell is of great importance for clinical applications of male infertility. Sperm tracking is quite complex due to cell collision, occlusion and missed detection. The goal of this study is simultaneous tracking of multiple human sperm cells. In the first step in this research the frame difference algorithm is used for background subtraction. There are some limitations to select an appropriate threshold value since the output accuracy is strongly dependant on the selected threshold value. To eliminate this dependency, we propose an improved nonlinear diffusion filtering in time domain. Nonlinear diffusion filtering is a smoothing and noise removing approach that can preserve edges in images.Many sperms that move with different speeds in different directions eventually coincide. For multiple tracking over time, an optimal matching strategy is introduced that is based on the optimization of a new cost function. A Hungarian search method is utilized to obtain the best matching for all possible candidates. Results show 3.24% frame based error in dataset of videos that contain more than 1 and less than 10 sperm cells. So the accuracy rate was 96.76%. These results indicate the validity of the proposed algorithm to perform multiple sperms tracking.

Locally Linear Model Tree (LOLIMOT) algorithm proposed by Nelles deals with local linear nearo-fuzzy models that is based on divides-and-conquer strategy that a complex modeling problem is divided to a number of smaller and thus simpler sub problems. So the characteristic of such a neuro-fuzzy model depends on division strategy for the original complex problem. For finding the best output the algorithm divides the problem to a number of local linear models (LLMs), then continues with finding the worst LLM and dividing it. LOLIMOT splits the local linear models into two equal halves with an axis-orthogonal decomposition strategy. In this paper a new approach based on extremeal optimization (EO) is used to optimize the structure of LOLIMOT. Simulation results show the effectiveness of the enhanced LOLIMOT to have a higher precision with optimal number of neurons.

Breast cancer is one of the leading causes of deaths among women. Mammography is currently the best method for early detection. Due to the breast tissue type and different kinds of lesions, by using low dose x-ray in mammography, the detection of lesions in mammograms becomes very ambiguous and a tedious work. Early detection is the most effective ways to reduce the mortality rate. Our main aim in this paper is detection and recognition of tumors in digital mammograms. Mammograms usually have a large size so the processing of the entire mammogram takes a lot of time. To reduce the size and therefore the processing time and also to decrease the False Positive Rate, a two-step algorithm is used. At the first step some unimportant regions such as background and pectoral muscle are eliminated and at the second step an ROI detection algorithm is proposed which extracts the most likely regions to tumors. To recognize the tumors in the detected regions, some features are extracted from each region. To find the most effective features for tumor detection, several data mining feature extraction and feature selection methods are used and then compared. To increase the performance and reduce the number of features a GA based algorithm is proposed. Finally, SVM is used as our classifier, because it has the best results in comparison with other tools in our application. Experimental results show that the performance of proposed methods is better than other previous methods. The True Positive Rate using SVM is 94.59% and the False Positive Rate is 22.95%.