Hepatocellular carcinoma (HCC) comprises a major subtype of primary liver cancer that
could be diagnosed earlier using computed tomography (CT) examination. Video compression reduces
the size of multi-frame data and induces blocking effect. This study aims to examine if the blocking
effect alters the performances of HCC tumor detection and hepatic lesion classification in CT images.
H.264 is considered in this study because it can compress 14-bit grayscale multi-frame data that is
compatible with the image requirement of CT. A range of quantization parameters (QP) was determined
in a phantom study. With this QP range, the trained support vector machines (SVM) based on
image features were applied to test 20 HCC cases and 21 normal cases. The SVM performed significantly better than the
random classification for detecting HCC in images compressed with the QP levels of interest. Images compressed with
QP1 yielded the best performance. The same range of QP levels was applied to 15 lesion-bearing images. The image quality
indices of these images were calculated to form the feature vectors. The clustering of these feature vectors identified at
least six clusters. The association of the lesion classes with the identified clusters was found significant for all QP levels
of interest. QP5 and QP9 yielded higher association than QP-3 and QP1. The findings proved a novel application of H.264
compression for enhancing the hepatic lesion detection classification in CT images.
Keywords: Computed tomography, hepatic lesion, Hepatocellular carcinoma, video compression.
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