The venipuncture, the catheterization and intravenous (IV) injections are some of the common procedures in the clinical practice. The location of the veins may be complex in some patients. In this paper a system able to enhance the vein distribution in a patient’s forearm in order to help, in future works, to locate the veins in a non-invasive way and accomplish the IV procedures, is described. To carry out this work a web cam was used, the filter that blocks out the infrared light has been removed and replaced for one who does not. To increase the vein detection an array of infrared LEDs (830 nm) was attached. The resulting images were processed using the adaptive histogram equalization and then classified by two methods, the first one based on the Fuzzy C-Means Algorithm, and the secondbased in a Bayesian probabilistic model. For the image acquisition, the anterior-exterior regions of the left and right forearm of each subject were considered to generate a data base. This system also has relevance in the detection ofvaricose veins since is able to monitor the vein dilatation.
This paper presents a system that allows defining the vein patterns of a person's forearm and hand. In order to accomplish this, infrared (IR) images of the region of interest were registered. The main goal is to help in vein detection, to aid in procedures like intravenous catheter or venipuncture, in a non-invasive way. In the image acquisition protocol, the anterior and posterior compartments of the left and right forearms of each subject were considered to create an image database each one containing the anthropometric data of the subject. A GUI was developed to allow recording an image by gender, age, weight, high and blood group and to allow two filtering options; the standard histogram equalization or fuzzy equalization. Finally, the user is able to choose and apply one of the two classification methods: one using the Fuzzy C-means algorithm, and the other using a Bayesian probabilistic model.
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