Multidimensional indexing technique for medical images retrieval

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.

Dynamics of unidimensional and multidimensional inequality in Pakistan

Purpose The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014. Design/methodology/approach The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality. Findings The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan. Originality/value This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.