Application of morphological image processing to remove hair noises while enhancing patterns of near-Infrared human vein images

nder NIR illumination, veins appear as dark lines while skin and fat tissues 
appear as lighter region, hence they are distinguishable. Developing a vein finder 
system using NIR techniques [7] often faces a challenge, which is the low contrast 
images that are generally present by light scattering. Enhancement of the contrast 
of vein images is possible by image processing. Recent years, typical image 
processing methods to enhance vein images have been used by many groups, both 
Vietnamese [2, 5, 8] and international [3, 9], employed common procedures 
including: application of median filtering, local histogram equalization following 
by threshold filtering and sometimes Gaussian filtering. 
 However, in some cases, hairs on hands or legs of a human can be dense and 
form a noise in the NIR vein images. The black hair acts just like a black body and 
can absorb the full spectrum of light. Hence it is almost impossible to separate hair 
noises/artifacts from vein patterns in NIR vein image by changing the optical 
Journal of Military Science and Technology, Special Issue, No.72A, 5 - 2021 69 
 Biomedical Physics 
configurations of the vein finder system. The hair noises also turn up as continuous 
and thin lines like vein lines. Most of the conventional image processing 
procedures appear to be less effective in removing noises from hairs. In this work, 
we propose new image processing procedures using additional morphological 
image processing steps to minimize (or to remove completely in some cases) noise 
effect of hairs, while preserving the continuous vein lines. To demonstrate the 
advantage of our approach, we construct a near-infrared vein imaging device to 
acquire vein images, and apply and compare the conventional image processing 
procedures versus ours. 
 2. MATERIALS AND METHODS 
2.1. A near-infrared vein imaging device 
 Figure 1. Diagram of the working principles of our near-infrared vein imaging 
 device (left side) and picture of the real device (right side). 
 We have constructed a near-infrared vein imaging device, which consisted of a 
light source (850-nm or 950-nm LEDs), a camera to capture the vein images, a 
computer Raspberry Pi 4 (model B) to process the digital image and a screen to 
display output vein images (figure 1). The image processing program is developed 
in Python language with open source library OpenCV and can be loaded and run 
on the Raspberry Pi 4. 
2.2. Conventional Image Processing Procedures 
 Figure 2. Diagram of the typical image processing procedures 
 to enhance vein image. 
 Typical image processing methods to enhance vein images often composes of 
various procedures (figure 2). Median filtering is a nonlinear method used to 
remove noise from images without eliminating the edges and lines. Specifically, 
the median filter works by replacing each pixel of the image with the median of all 
pixels in a neighborhood. Local histogram equalization following by threshold 
filtering can be applied then. After processing, the visual contrast and the 
sharpness of vein images can be enhanced but one problem remains unchallenged 
is the noise caused by hairs. 
2.3. Proposed Image Processing Procedures 
70 P. V. Quan, , D. N. Thuan, “Application of morphological image  human vein images.” 
Research 
 To minimize noise effect of hair (in some case can remove hair noises 
completely) in output vein images, we employ morphological image processing 
after adaptive histogram equalization process (figure 3). The morphological image 
processing steps include opening, closing and dilation algorithms. 
Figure 3. Diagram of our image processing procedures employed morphological 
 image processing to enhance vein image. 
2.4. Morphological Image Processing Algorithms 
 Morphological image processing is the processing of images based on the 
structure and shape of the object [10]. Its language is set theory. By choosing the 
appropriate structural element (set B), it is possible to construct the morphology 
sensitive to the special shapes in the input image (set A). In our work, 
morphological image processing algorithms are applied for binary images. Most 
morphological image processing algorithm can be defined from two basic 
operations: dilation and erosion. 
 The dilation of by , denoted as , is the set of all displacements z (z is 
the center of ’s structuring element on ), such that and overlap by at least 
one element. It can be defined as: 
 { [( ) ] } (1) 
 Meanwhile, the erosion of by , denoted as is the set of all points z, 
such that , translated by z, is contained in . It is defined as: 
 { } (2) 
 While dilation tends to expand the image’s boundaries, erosion tends to reduce 
these boundaries, even remove the small ones. 
 If we perform an erosion of A by B, followed by a dilation of the result by B, we 
then obtain opening of A by B, denoted as 
 (3) 
 Similarly, if a dilation is performed first, followed by an erosion, we then obtain 
closing of A by B, denoted as: 
 (4) 
 Opening generally smoothes the contour of an object and eliminate thin 
protrusions, while closing tends to fill narrow gaps and smoothen lines within 
the contour. 
 3. RESULTS AND DISCUSSION 
3.1. Vein images captured by our near-infrared vein imaging device and 
enhanced by conventional image processing procedures 
Journal of Military Science and Technology, Special Issue, No.72A, 5 - 2021 71 
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 Firstly, we apply conventional image processing procedures used by various 
research group to enhance the vein image of a human’s upper hand (figure 4). Although 
salt-pepper noise can be reduced by median filtering and contrast of the images can be 
improved after processing, hairs remain and largely affect the output images. 
 Figure 4. Vein image of a human’s upper hand after typical image processing 
 procedures, showing hairs remain and affect output images. 
3.2. Effect of the morphological image processing algorithms 
 To minimize hairs on the vein images, we introduce a new image processing 
approach with employs consecutive morphological image processing steps 
including opening, closing and dilation algorithms. Effect of each processing step 
can be elucidated as follows: 
3.2.1. Effect of the closing algorithm 
 0 0 1 0 0 
 1 1 1 1 1 
 B  1 1 1 1 1 
 1 1 1 1 1 
 0 0 1 0 0 
 Figure 5. Effect of the structuring element B’ size in closing algorithm on vein 
 image of a human’s upper hand. The size of each image is (480x640) pixels. 
 Median filtering can only remove small dot-shaped noise, while thin and long 
line-shaped noises are not affected. To remove these line-shape noises, we first 
apply closing algorithm. The size and shape of structuring element B is the most 
important factor in the morphological image processing method. Edged shapes like 
a triangle or rectangular will often break continuous lines, hence we choose an 
ellipse shape. We then apply closing algorithm with various sizes of structuring 
element B order to find the optimal one. An example of structuring element B is an 
ellipse with the size of (5x5) pixels is in figure 5. 
 Figure 5 shows effect of the ellipse’s size on the closing algorithm on vein 
images of a human’s upper hand. It is clear that the small size of the ellipse – (1x1) 
or (3x3) pixels – results in little effect of hair removal. While the big size of the 
ellipse – (9x9) or (11x11) – pixels) not only removes hair noise, but also breaks 
some continuous line vein and removes some broken vein lines which are important 
information. Hence, we choose the size of the structuring element B is an ellipse at 
(5x5) size for the finest effect of hair removal while preserving important 
information. Applying closing algorithm helps reduce significant line-shaped 
72 P. V. Quan, , D. N. Thuan, “Application of morphological image  human vein images.” 
Research 
noises, however, it also cut some of the thin-lined veins. A simple closing algorithm 
can not solve the problem and we have to use other following algorithms. 
3.2.2. Effect of the opening algorithm 
 Using the same principle for testing resulted image with different sizes of 
structuring element B, we derive the optimal size of the ellipse for the following 
algorithms. To restore the broken thin-lined vein (figure 6), we apply opening 
algorithm while structuring element B is an ellipse with the size of (9x9) pixels. 
3.2.3. Effect of the dilation algorithm 
 After consecutive opening and closing algorithm, some hair noises are removed. 
However, the vein’s lines are quite thick. To make those line thinner, we apply 
dilation algorithm while structuring element B is an ellipse with the size of (6x6) 
pixels (figure 6). 
 Figure 6. Effect our morphological image processing procedure, 
 including consecutive closing, opening and dilation algorithms 
 on vein image of a human’s upper hand. 
3.2.4. Effect of our new image processing procedures 
 Figure 7. Vein image of a human’s upper hand after our new image processing 
 procedures, showing significant hair noises (dot lines, in red) can be removed. 
 After applying our new morphological image processing procedures, we can 
remove significant hair noises as shown in figure 7. Although the vein lines 
become thinner, all continuous vein lines in the images are maintained. Comparing 
with output images processed by conventional image processing procedure (figure 
4), our result clearly shows the advantage of our new image processing procedures 
in removing hair noises while maintaining continuous vein lines. 
3.3. Vein images from various part of human hands enhanced by our image 
processing procedures 
 To verify effect of our new image processing approach, we have taken images 
Journal of Military Science and Technology, Special Issue, No.72A, 5 - 2021 73 
 Biomedical Physics 
from other parts of human hands including wrist (figure 8) and arm (figure 9). 
These images further confirm the advantage of our new image processing 
procedures in removing hair noises while enhancing vein lines and maintaining 
their borders with surrounding tissues. However, some discontinuous vein lines also 
are affected. With further optimization, our new image processing procedures can 
be widely applied to enhance vein images from various parts of the human body. 
 Figure 8. Vein image of a human’s wrist after our new image processing 
 procedures. Significant hair noises (dot lines, in red) can be removed 
 while a discontinuous vein line (dash line, in blue) is also affected. 
Figure 9. Vein image of a human’s arm after our new image processing procedures. 
 4. CONCLUSIONS 
 We have developed a new image processing approach employed morphological 
image processing to efficiently remove hair noises, and constructed a vein finder 
device to acquire vein images and compare our new procedures with conventional 
ones. We have studied the effects of the elements ellipse’ size in different 
morphological image processing steps and optimized them to achieve the best 
enhancement effect. We show that our new image processing procedures have 
overcome most conventional ones in removing hair noises, enhancing vein lines 
while maintaining important information of the vein images. Moreover, our 
approach is applicable in vein images from various part of the human hand. Thus, 
the principle of our approach can be applied widely to enhance images from other 
parts of the human body and in many other vein finder systems. 
 Acknowledgement: This research is supported by the Vietnam’s Ministry of Science and 
Technology under Project ĐTĐL.CN-40/19. 
74 P. V. Quan, , D. N. Thuan, “Application of morphological image  human vein images.” 
 Research 
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 TÓM TẮT 
 ỨNG DỤNG THUẬT TOÁN XỬ LÝ ẢNH HÌNH THÁI ĐỂ LOẠI BỎ NHIỄU 
 DO LÔNG GÂY RA NHẰM NÂNG CAO CHẤT LƯỢNG ẢNH TĨNH MẠCH 
 TỪ KỸ THUẬT THU ẢNH HỒNG NGOẠI GẦN 
 Gần đây, các thiết bị nhận ảnh tĩnh mạch đã được nghiên cứu rộng rãi và 
 có nhiều ứng dụng thực tế trong y tế và bảo mật. Một trong những hướng 
 nghiên cứu chính phát triển thiết bị này là các thuật toán xử lý ảnh giúp tăng 
 cường ảnh tĩnh mạch. Một số thuật toán xử lý ảnh như lọc trung vị và cân 
 bằng hoành đồ tương ứng thường được sử dụng và cho kết quả tốt. Tuy nhiên, 
 trong một số trường hợp khi có nhiều lông ở vị trí chụp ảnh và gây ra nhiễu, 
 thì các phương pháp này chưa thật hiệu quả trong loại bỏ nhiễu. Trong nghiên 
 cứu này, chúng tôi đưa ra một cách tiếp cận mới sử dụng thêm thuật toán xử lý 
 ảnh hình thái để loại bỏ hiệu quả các nhiễu do lông gây ra. Chúng tôi đã chế 
 tạo thành công thiết bị thu ảnh tĩnh mạch và sử dụng chúng để tối ưu các bước 
 xử lý ảnh của mình. Ảnh hưởng của kích thước, hình dạng của cấu trúc thành 
 phần trong các bước xử lý ảnh hình thái khác nhau đã được khảo sát và tối ưu 
 hóa để đạt được hiệu quả tốt nhất. Cách tiếp cận của chúng tôi có thể được áp 
 dụng cho nhiều thiết bị nhận ảnh tĩnh mạch khác và nâng cao chất lượng hình 
 ảnh tĩnh mạch chụp từ các vùng khác nhau trên cơ thể. 
 Từ khóa: Xử lý ảnh hình thái; Tăng cường ảnh; Ảnh tĩnh mạch người; Hồng ngoại gần. 
 Received 31th August 2020 
 Revised 18th November 2020 
 Accepted 10th May 2021 
 Author affiliations: 
 1 Department of Optical Devices, Le Quy Don Technical University; 
 2 Institute of Materials Sciences (IMS), Vietnam Academy of Science and Technology. 
 *Corresponding author: thuandn@ims.vast.ac.vn. 
 Journal of Military Science and Technology, Special Issue, No.72A, 5 - 2021 75