Modified image enhancement algorithm for dorsal hand veins imaging
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Date
2018-06
Authors
Gan, Siew Ling
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Abstract
Peripheral intravenous (IV) access is an important issue in daily practice in hospital
as it is a common practice in the medical field. However, it will be a difficult task if the dorsal
hand veins are not clear or obvious for intravenous access. The poor visibility of dorsal vein
may result in wrong puncturing which causes patient to suffer from pain and even will lead
to permanent damage of vein. Hence, a number of imaging methods have been implemented
to expose the veins. At present, there are limited literature regarding the specific study to
justify the enhancement algorithm which can perform effectively for dorsal hand vein
imaging. Therefore, this research is set-up to develop a modified image enhancement
algorithm for dorsal hand vein. Firstly, the grayscale hand vein image obtained from NIR
imaging with noise undergoes grayscale enhancement by applying Contrast Limited
Adaptive Histogram Equalization (CLAHE). Then, the adaptive thresholding method is
implemented on the filtered grayscale image for vein pattern segmentation purpose. After
image segmentation, the input image is converted to binary image. The noisy binary vein
pattern is then enhanced using a combination of Feed-Forward Neural Network (FFNN),
Area Opening (AO) and Binary Median Filter (BMF). Finally, the enhanced image is
evaluated by examining the image’s sensitivity, specificity and accuracy of the enhanced
image through comparison with the ground truth images. The evaluation results between
modified image enhancement algorithm are compared with the existed algorithm. The
evaluation results shows that the AO-FFNN-BMF sequence produces the highest sensitivity,
specificity and accuracy for both input images. The proposed technique has produced the
clearest vein patterns in terms of connectivity and smoothness than the other binary
enhancement techniques.