Background: Claustrophobia or fear of closed spaces is the most common of phobias that istypically categorized as an anxiety disorder. Different methods have been proposed for treatmentof phobias that one of the most recent and successful of these methods is applying virtualreality (VR) technology and simulating computer-generated environment. In this regard, thepurpose 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 computerand 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, averageage 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 databy 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 promisingresults demonstrate that the game has an appropriate performance and can help to treat the Claustrophobia.

This paper presents a simple and efficient computer-aided diagnosis method to classify Chronic Myeloid Leukemia (CML) cells based on microscopic image processing. In the proposed method, a novel combination of both typical and new features is introduced for classification of CML cells. Next, an effective decision tree classifier is proposed to classify CML cells into eight groups. The proposed method was evaluated on 1730 CML cell images containing 714 cells of non-cancerous bone marrow aspiration and 1016 cells of cancerous peripheral blood smears. The performance of the proposed classification method was compared to manual labels made by two experts. The average values of accuracy, specificity and sensitivity were 99.0 %, 99.4 % and 98.3 %, respectively for all groups of CML. In addition, Cohen's kappa coefficient demonstrated high conformity, 0.99, between joint diagnostic results of two experts and the obtained results of the proposed approach. According to the obtained results, the suggested method has a high capability to classify effective cells of CML and can be applied as a simple, affordable and reliable computer-aided diagnosis tool to help pathologists to diagnose CML.