Blood Group detection using Image processing

Published on 18 May 2022. Written by Palvi Soni

Hospitals need blood for saving patients life and knowing blood type during an emergency situation can save the life of the patient before any mishappening. Technology is becoming so advanced these days for stop happening of human errors, technology has developed image processing method for detecting blood group. This method basically eliminates transfusions. We have basically ABO blood typing system. The types of blood groups are Type A, Type B, Type O and Type AB.

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SLNOTE


Technology used:

MATLAB is used for performing this task. MATLAB has various default functions that make it suitable for performing image processing techniques effectively.

Proposed Methodology of Project

1. Acquire Blood images dataset
2. Input blood Image
3. Extract colors from selected Image
4. Binarisation of Images
5. Apply Morphological functions
6. Apply Hue, saturation and lightness
7. Apply ROI Feature
8. Apply Clustering Technique
9. Detection of blood groups
10. Clear data and Refresh
11. Exit

SLLATEST
Acquire Blood images dataset: Firstly, for performing any task related to image processing group of images is needed, so we need to acquire a dataset of blood types that can be taken manually from hospitals or we can also gather it from the internet as well.

Input blood image:  We need to import image dataset into a folder and then we need to select any image of our choice so that we would able to perform image processing operations on it.

Extract colours from the selected image: Color plane extraction allows to extract relevant information regarding color and eliminates unwanted information from it. Due to the highest value in RGB panel, only green color intensity is selected

Binarization of Images: For performing binarization of images, the resulted images from color plane extraction are now converted into binary images, for this, we use thresholding method, you can use any threshold method like otsu, niblack, local thresholding depending on your requirements.

Apply Morphological functions: Morphological operations works so well on binary images. This will manage the ordering of pixels; the user can modify binary image according to their requirements. It basically helps to mitigate noise in the images.

Apply Hue, saturation and lightness: This feature is applied to enhance more features by extracting the background colors of an image to achieve appropriate results.

Apply ROI feature: ROI basically stands for the region of interest, by applying this function we can extract relevant region on the basis of which we can easily classify the blood groups. It mainly focuses on the relevant region and discards the irrelevant part.

Apply Clustering techniques: We can now make clusters based on the similarity of features. Group of similar patterns are clusterized in one cluster and others are in another cluster.

Detection of blood groups: This is used to detect a particular blood group selecting from different clusters. Now from particular image blood group is detected whether it is A+ or O+ etc. A simple pop-up message will appear showing blood group below selected image.

Clear data and Refresh: You can test this code on different images, for performing this clear out previous data and click on refresh option and select a new image of your choice.

Exit: By using the option you can exit from the application whenever you want to exit.

SLDYK
Kit required to develop Blood Group detection using Image processing:

• No kit required

Technologies you will learn by working on Blood Group detection using Image processing:

• MATLAB
• Image Processing

Any Questions?