Plasma cells are developed from B lymphocytes, a type of white blood cells that is generated in the bone marrow. The plasma cells produce antibodies to fight with bacteria and viruses and stop infection and disease. Multiple myeloma is a cancer of plasma cells that collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. The definitive diagnosis of multiple myeloma is done by searching for myeloma cells in the bone marrow slides through a microscope. Diagnosis of myeloma cells from bone marrow smears is a subjective and time-consuming task for pathologists. Also, because of depending on final decision on human eye and opinion, error risk in decision may occur. Sometimes, existence of infection in body causes plasma cell's increment which could be diagnosed wrongly as multiple myeloma. The computer diagnostic process will reduce the diagnostic time and also can be worked as a second opinion for pathologists. This study presents a computer-aided diagnostic method for myeloma cells diagnosis from bone marrow smears. At first, white blood cells consist of plasma cells and other marrow cells are separated from the red blood cells and background. Then, plasma cells are detected from other marrow cells by feature extraction and series of decision rules. Finally, normal plasma cells and myeloma cells could be classified easily by a classifier. This algorithm is applied on 50 digital images that are provided from bone marrow aspiration smears. These images contain 678 cells: 132 normal plasma cells, 256 myeloma cells and 290 other types of marrow cells. Applying the computer-aided diagnostic method for identifying myeloma cells on provided database showed a sensitivity of 96.52%; specificity of 93.04% and precision of 95.28%.

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Background:Claustrophobia or fear of closed spaces is the most common of phobias that is typically categorized as an anxiety disorder. Different methods have been proposed for treatment of phobias that one of the most recent and successful of these methods is applying virtual reality (VR) technology and simulating computer-generated environment. In this regard, the purpose of this research is design and development of a software game called “Claustrophobia Game” for treatment of claustrophobia using VR.Methods:In the Claustrophobia Game project, two closed spaces, including an elevator and a magnetic resonance imaging (MRI) device, were designed and implemented in the form of a VR computer game. To design this game, environments and scenario of the game were prepared in collaboration with a psychiatrist expert. Implementation of the software game was developed in the unity three-dimensional (3D) game engine and the programming of it was done by the C# language. In addition, a personal computer and the Oculus Rift VR glasses were utilized for running and testing the Claustrophobia Game.Results:To evaluate, we tested the game by 33 participants (14 men, 19 women, average age 24.6 years). In this regard, the Claustrophobia Game was considered from two aspects: psychology and playability using two questionnaires. Statistical analysis of the obtained data by the Excel software showed that all playability factors were “good” performance. In addition, the mean of obvious anxiety was decreased after playing the game.Conclusion:The promising results demonstrate that the game has an appropriate performance and can help to treat the Claustrophobia.

In recent years, active contour models (ACM) have been considered as powerful tools for image segmentation and object tracking in computer vision and image processing applications. This article presents a new tracking method based on parametric active contour models. In the proposed method, a new pressure energy called ' 'texture pressure energy' ' is added to the energy function of the parametric active contour model to detect and track a texture target object in a texture background. In this scheme, the texture features of the contour are calculated by a moment-based method. Then, by comparing these features with texture features of the target object, the contour curve is expanded or contracted to be adapted to the object boundaries. Experimental results show that the proposed method is more efficient and accurate in the tracking of objects compare to the traditional ones, when both object and background are textures in nature.

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This paper presents a fully automated approach to detect the intima and media-adventitia borders in intravascular ultrasound images based on parametric active contour models. To detect the intima border, we compute a new image feature applying a combination of short-term autocorrelations calculated for the contour pixels. These feature values are employed to define an energy function of the active contour called normalized cumulative short-term autocorrelation. Exploiting this energy function, the intima border is separated accurately from the blood region contaminated by high speckle noise. To extract media-adventitia boundary, we define a new form of energy function based on edge, texture and spring forces for the active contour. Utilizing this active contour, the media-adventitia border is identified correctly even in presence of branch openings and calcifications. Experimental results indicate accuracy of the proposed methods. In addition, statistical analysis demonstrates high conformity between manual tracing and the results obtained by the proposed approaches.

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