Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

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Auto correction functionality is very popular in search portals. Its principal purpose is to correct common spelling or typing errors, saving time for the user. However, when there are millions of strings in a dictionary, it takes considerable amount of time to find the nearest matching string. Various approaches have been proposed for efficiently implementing auto correction functionality. All of these approaches focus on using suitable data structure and few heuristics to solve the problems. Here, we propose a new idea which eliminates the need for calculating edit distance with each string in the dictionary. It uses the concept of Ngram based indexing and hashing to filter out irrelevant strings from dictionary. Experiments suggest that proposed algorithm provides both efficient and accurate results.