Akses kepada salur darah merupakan isu penting di hospital kerana ia amat biasa dalam perubatan. Walau bagaimanapun, corak salur darah tangan yang tidak jelas akan menyukarkan proses akses kepada salur darah. Corak salur darah yang tidak jelas akan menyebabkan kesilapan berlaku semasa suntikan dijalankan dan kesilapan tersebut akan menyebabkan kesakitan dan kecederaan salur darah yang tidak dapat dibaik pulih. Tiada kajian khusus yang menjustifikasikan kaedah pemprosesan yang berkesan ke atas imej corak salur darah tangan. Oleh itu, kajian ini dijalankan untuk membangunkan sistem pemprosesan imej corak salur darah tangan yang telah diubah suai. Permulaannya, imej salur darah tangan kelabu yang diperolehi daripada pengimejan NIR dengan menggunakan penyamaan histogram penyesuaian kontras terhad (CLAHE). Kemudian, corak salur darah tangan diruas berdasarkan nilai ambang tempatan. Selepas proses ini, imej bertukar kepada imej binari. Corak urat tangan dipertingkatkan dengan gabungan Pembetul Piksel Rangkaian Neural Buatan, pembukaan dan penapis binari median. Selepas itu, penilaian prestasi kaedah pemprosesan telah dibuat berdasarkan kepekaan, kekhususan dan ketepatan. Perbandingan dilakukan antara keputusan penilaian prestasi kaedah pemprosesan yang telah diubah suai dan kaedah yang telah wujud. Keputusan penilaian prestasi menunjukkan susunan AO-FFNN-BMF menghasilkan kepekaan, kekhususan dan ketepatan yang paling tinggi bagi kedua-dua jenis imej. Kaedah pemprosesan imej yang dicadangkan telah menghasilkan corak salur darah yang paling jelas apabila dibandingkan dengan kaedah lain-lain.
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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.
Modified image enhancement algorithm for dorsal hand veins imaging / Gan Siew Ling