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    Adaptive quantization with Fuzzy C-mean clustering for liver ultrasound compression
    (2014-12-16)
    Sombutkaew, Rattikorn
    ;
    Kumsang, Yothin
    ;
    Chitsobuk, Orachat
    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.
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    Medical image compression and quality assessment
    (2013-01-01)
    Suapang, Piyamas
    ;
    Thongyoun, Methinee
    ;
    Chivapreecha, Sorawat
    In this research proposed to compressed medical images in single frame file format with two different techniques - JPEG and JPEG2000. The significant advantage of JPEG2000 over normal JPEG is that the error from JPEG2000 compression is smaller than the error from JPEG. Nevertheless, both methods share a similar mishap; when the compression ratio increases, they both generate more error than the processes on lower compression ratio do. What's more, the research proposed a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers and using a set of key features extracted from the original and test images. Experimental results, using JPEG and JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. The predicted MOS values have a linear correlation coefficient of0.9543, a Spearman ranked correlation of 0.9591.