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Calculating the presence of blood cell using computer vision

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The Blood cells white, red and platelets are important part of the immune system. These cells help fight infections by attacking bacteria, viruses, and germs that invade the body. White blood cells originate in the bone marrow but circulate throughout the bloodstream, while red blood cell helps transport oxygen to our body and platelets are tiny blood cells that help your body from clots to stop bleeding. Accurate counting of those may require laboratory testing procedure that is not usual to everyone. Generating codes that will help counting of blood cells that produce accurate response via images gives a relief on this problem. In this study, the images were processed and a blob detection algorithm was used to detect and differentiate RBCs from WBCs, PLATELETs. A cell counting method was also used to provide an actual count of the RBCs, WBCs and PLATELETs detected. The automation comes with a graphical user interface backed-up with a working database system to keep the records of the users (e.g. patients, respondents). The performance of the system was statistically described as accurate compared to the manual method of counting. Results show an accuracy of 100% for platelet, 96.32% for RBCs and 98.5% for WBCs. Hence, the proposed system can benchmark with the manual methods of detection and counting of PLATELETs, RBCs and WBCs in blood samples


Platelets are one of the blood cells that stops the bleeding in the body from blood clotting. Platelets can detect if any blood vessels are damaged. Red blood cells are also tiny blood cells that is also important in the health of human through carrying fresh oxygen throughout the body whereas white blood cells helps protect the body from infections. Complete Blood Count (CBC) involves blood testing to determine the healthiness of the major components of blood which are platelets, red blood cells and white blood cells. Abnormalities of result based from references of normal count of cells may indicate an underlying medical condition that needs further evaluation. For this past few years, CBC counting is one of the most studied area of research due to accuracy problem. Laboratories in the hospital in the Philippines are still using the traditional method of counting blood cells. This was done in either manual method through hemocytometer or by automated method through flow-cytometer. In this study, it uses images of the blood to calculate the number of cells since research on medical images is new technology. Image processing is a method which involves signal processing and mathematical procedure to change the image into another form of desired image. Image analysis is the extraction of significant information from an image. Hence, this paper does not involve image processing only but analysis as well. Nowadays, there are many ways of image processing and analysis of blood cell images. However, the quest for the highest accuracy is still one of the aims of the researchers. With so many studies, the researchers will present another way of counting blood cells through the use of strong level of algorithm with the help of python OpenCV programming language. This study used colour filtering to keep a specific hue while desaturating the rest of the image. It also involves image segmentation to convert the image into multiple parts to identify which of the cells are platelets, red blood cells or white blood cells. Blob detection plays important role in this study which primarily detects the differences of each blood cells before the cells will be counted.


Existing System:

Before this we have a tendency to accustomed realize for color extraction and classifies by the Support vector machine by this we have a tendency to cant say at the acceptable granules square measure gift and what kind of  Counting of Blood cells.

Proposed System:


Simple Blob Detector, as the name implies, is based on a rather simple algorithm described below. The algorithm is controlled by parameters (shown in bold below)  and has the following steps. Scroll down to know how the parameters are set.

  1. Thresholding: Convert the source images to several binary images by thresholding the source image with thresholds starting at the min threshold. These thresholds are incremented by the threshold Step until max threshold. So the first threshold is the min threshold, the second is min Threshold + threshold step, the third is min Threshold + 2 x threshold step, and so on.
  2. Grouping: In each binary image, connected white pixels are grouped together.  Let’s call these binary blobs.
  3. Merging: The centers of the binary blobs in the binary images are computed, and blobs located closer than min Dist Between Blobs are merged.
  4. Center & Radius Calculation:  The centers and radii of the new merged blobs are computed and returned.


Block Diagram:

Blood Cell Counting




  • Information is more from input
  • Accuracy is heavy


Software Requirement:

  • Python idle
  • opencv
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