Smartphone aided real-time blood vein detection system

Abstract

This paper aims to develop a real-time integrated system for the detection of the blood vein utilizing an Android Mobile App. The system is intended to be a low cost solution for medical teams at clinics, emergency rooms and hosptials. The system reduces the enjuries incurred due to inaccuracies during the process of frequent needle injection when blood vein is not visible during patient’s skin inspection. Illuminated infrared light in the blood cells of the vein is absorbed due to the manifestation of the Haemoglobin in blood and the IR non-blocking camera can capture the vein patterns in the IR light spectrum. Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was used to enhance the pattern of the vein in the Android application developed using OpenCV3. Developed system can detect the veins up to 7mm underneath of human skin in real time with a frame rate of 25fps. This is a far better improvement than commercial systems that can detect veins only below 10mm underneath the skin. Moreover, this system not only focused on needle infusion but also it can be used to indicate the place of bleeding for the clots from the human body strokes, etc. in the upper layer of skin. It can also be used to detect measure liquids in encapsulated in confined dark bottles, for example, liquid chemical pouring into the bottles in the chemical companies, liquid medicine pouring to bottles, etc. The system can be further developed to detect skin infection and other dermatological diseases underneath the skin.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…Therefore, the NIR channel was used to refine the image's contrast. The HE method enhances the image by distributing the image brightness levels equally across the brightness scale [1], [11], [15], [21], [44], [53], [54], [72], [94], [119], [136], [140], [145], [150], [159], [168], [169], [172], [175]. Furthermore, the intensity of the contrast enhancement method is measured through the root mean square (RMS), where the higher the RMS value, the better the contrast image [22], [35], [48], [178]- [181].…”

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

“…To overcome this limitation, the works of [16,17] have used adaptive histogram equalization (AHE); however, AHE over-amplify noise in the resultant image. Later, advancement on HE and AHE, referred to as CLAHE has produced outstanding results for contrast enhancement of dorsal hand vein images [18][19][20]. It restrains the problem of noise over-amplification in AHE using a clip limit criterion.…”

Improving radiographic image contrast using multi layers of histogram equalization technique

“…Mohammed et al [10][11][12][13] focused on using HE to enhance many types of images (breast cancer, fingerprint, finger-vein and face) with good results, but also using one layer of this technique. Ahmed et al [14] proposed a real-time significant model for the detection of the blood vein using mobile application under android with CLAHE technique as one layer of enhancement image. Shamsudeen et al [15][16][17][18] used HE with other techniques to enhance images in order to get more contrast image, but no studies using HE with many layers.…”