DocumentCode :
1768517
Title :
Adaptive quantization with Fuzzy C-mean clustering for liver ultrasound compression
Author :
Sombutkaew, Rattikorn ; Kumsang, Yothin ; Chitsobuk, Orachat
Author_Institution :
Fac. of Eng., King Mongkut´s Inst. of Technol. Ladkrabang, Bangkok, Thailand
fYear :
2014
fDate :
22-25 Oct. 2014
Firstpage :
521
Lastpage :
524
Abstract :
With the massive increment of patients´ medical information and images also limitation in transmission bandwidth, it is a challenging task for developing efficient medical information and image encoding techniques for digital picture archiving and communications (PACS). In order to achieve higher encoding efficiency, this research proposes adaptive quantization via fuzzy classified priority mapping. Image statistical characteristics are used as key features for Fuzzy C-mean clustering. The derived priority map is used to identify levels of importance for each image area. The significant candidates of irregular liver tissues, which need special doctor´s attention, will be assigned with higher priority than those from the regular ones. The higher the priority, the greater the number of bits assigned for encoding. An analysis of suitable quantization step size has been conducted. With the selection of appropriate quantization parameters for each priority level, the blocking artifacts can be greatly reduced. This results in quality improvement of the reconstructed images while the compression ratio remains reasonably high.
Keywords :
PACS; biological tissues; biomedical ultrasonics; fuzzy set theory; image classification; image coding; image reconstruction; liver; medical image processing; pattern clustering; statistical analysis; PACS; adaptive quantization; digital picture archiving and communications; doctor attention; fuzzy c-mean clustering; fuzzy classified priority mapping; image encoding techniques; image reconstruction; image statistical characteristics; liver ultrasound compression; patient medical information; quantization parameters; transmission bandwidth; Encoding; Image coding; Fuzzy C-mean Clustering; JPEG; Quantization table; Ultrasound Compression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Automation and Systems (ICCAS), 2014 14th International Conference on
Conference_Location :
Seoul
ISSN :
2093-7121
Print_ISBN :
978-8-9932-1506-9
Type :
conf
DOI :
10.1109/ICCAS.2014.6987834
Filename :
6987834
Link To Document :
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