Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties. Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide better performance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. The results show that using Type-2 PCM method provides better results

Scientometric Study of Research in Information Retrieval in Medical Sciences

Background: Mapping scientific trends is one of the most important missions of scientometric research for effective research. The main goal of this paper was to visualize and draw the intellectual and cognitive structures of information retrieval (IR) in the medical sciences using science mapping. Methods: In this cross-sectional scientometric study, we recruited all documents indexed in the Web of Science database with the topic of storing and retrieval of information in medical sciences. To analyze the results, 3 software, SciMAT-v1.1.04, VOSviewerv1.6.14, CitNetExplorerv1.0.0, were used. Results: Our results showed that most scientific productions in this field fall into 2 categories: (1) effective methods of organizing information and (2) application and operation of the IR system in the process of intelligent questioning and answering, and analyzing information behaviors of physicians and health professionals. The results showed that the similarity index increased over time from 0.43 to 0.71. Analysis of the findings showed that similarity measures, expert systems, concepts, experience, answers, and multimodel IR clusters were considered as mature and completely centralized clusters in the first quarter of the strategic chart. Conclusion: Because of the dramatic approximation of the vocabulary used by researchers and a relative slowdown in the growth rate of the subject's domain in the last decade, it seems necessary to pay attention to the expansion of the fields of IR and the application of its concepts in medical information sciences. Also, it can be recommended that designers of IR systems and techniques in medical information sciences pay more attention to human factors attentively to develop new technologies and tools. © Iran University of Medical Science