# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
###### Abstract  
This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people's lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of 99.6% in an average time of 250 ms.  
blood typing; image matching; scale invariant feature transform (SIFT); speed-up robust feature (SURF); oriented fast and rotated brief (ORB)  
18 March 2021 26 May 2021 4 June 2021 4 June 2021 4 June 2021 402 4 The majority of automatic blood typing approaches include three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, many techniques can be used to improve the input image for further processing, such as noise reduction or removal, color space transformation, brightness correction, morphological modification, and isolation of the region of interest (ROI) (i.e., in this case, the blood spot or part of it as the agglutinated clusters) from the background, among other techniques. In the feature extraction stage, the ROI is transformed into a string of descriptors called features (mainly as a number or a sequence of numbers) to be used for identification in the classification stage, where the ROI is recognized as agglutination or not. The classification and preprocessing techniques are common between the following studies. In the literature review below, we will concentrate on the feature extraction stage because it represents the fundamental step in the automatic typing process for blood discrimination. [PERSON] et al. [1] used the standard deviation (SD) calculated from blood images to determine their group. Four images are captured for the blood sample using a CCD camera (charge-coupled device) after mixing it with A, B, AB, and D antigens. Then, the images are analyzed through a sequence of operations using IMAQ vision software, which is an image processing software that was developed originally by National Instruments. The operations include color conversion and processing, manual thresholding, and morphological operations [1]. The approach was improved in [4], but was tested using only 24 blood samples. The standard deviation feature was also used by [PERSON] et al. [5] and [PERSON] et al. [6] after being extracted from the color image and the green band of the blood, respectively. Another study used support vector machine (SVM) to detect the agglutination based on the standard deviation as well [7]. [PERSON] et al. [8] isolate the blood spot from the background based on its luminance after transforming the RGB (red, green, and blue) input image into an HSV (hue, saturation, and intensity value) color model according to static color values. Then, they detect the blobs in the ROI and classify them (i.e., into agglutination or not) according to their area. However, these approaches suffer from many limitations such as having manual operations and a relatively long processing time of 2 min. Moreover, Section 2.2 shows that the standard deviation is not a discriminant feature; it cannot be used alone to discriminate between normal and agglutinated blood spots. Furthermore, the usage of the SVM by [PERSON] et al.