IMAGE PROCESSING BASED

Leukemia is diagnosed with complete blood counts which is by calculating all blood cells and compare the number of white blood cells (White Blood Cells / WBC) and red blood cells (Red Blood Cells / RBC). Information obtained from a complete blood count, has become a cornerstone in the hematology laboratory for diagnostic purposes and monitoring of hematological disorders. However, the traditional procedure for counting blood cells manually requires effort and a long time, therefore this method is one of the most expensive routine tests in laboratory hematology clinic. Solution for such kind of time consuming task and necessity of data tracability can be found in image processing techniques based on blood cell morphology. This study aims to identify Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia type M3 (AML M3) using Fuzzy Rule Based System based on morphology of white blood cells. Characteristic parameters witch extractedare WBC Area, Nucleus and Granule Ratio of white blood cells. Image processing algorithms such as thresholding, Canny edge detection and color identification filters are used.Then for identification of ALL, AML M3 and Healthy cells used Fuzzy Rule Based System with Sugeno method. In the testing process used 104 images out of which 29 ALL-Positive, 50 AML M3-Positive and 25 Healthy cells. Test results showed 83.65 % accuracy .

TJPRC, 2014

Image processing techniques are widely used in the domain of medical sciences for detecting various diseases, infections, tumors, cell abnormalities and various cancers. Detecting and curing a disease on time is very important in the field of medicine for protecting and saving human life. There are three types of primary cells in human body red blood cells, white blood cells and platelets. Their functionalities are different. In this paper red blood cells detection and counting is done and it is observed that which cells are normal and which are infected.

The measure of WBC and RBC Cells are very crucial to diagnose various diseases. Diseases like anaemia, leukaemia etc. can easily diagnose by calculation of WBC and RBC. Healthcare industries are focusing on the approach to generate report of blood cell count in fast and cost-effective way. Conventional method of manual measurement of red blood cell under a microscope yields incorrect results, consumes more time and very expensive. In market, there are numerous systems available for the automatic quantification of blood cells. These systems allow counting the number of different types of cells within the blood smear slides. The aim objective of this research is to produce a survey on computer vision system used image processing algorithms to detect and estimate the number of red blood cells in the blood sample image. In this project, image processing algorithms are used for counting of blood cells. Image processing algorithms involve six major steps: image acquisition, pre-processing, image enhancement, image segmentation, feature extraction and counting algorithm. In this project, segmentation, detection, and counting red blood cells in the blood sample image is carried out using Hough Transform, Roughest theory and KNN method.

The human blood consists of the RBCs, WBCs, Platelets and Plasma. The complete blood count defines the state of health. Blood is a health indicator therefore segmentation and identification of blood cells is very important. Complete Blood Count (CBC) includes counting of all the cells which determines person's health. The RBC and WBC count is very important to diagnose various diseases such as anemia, leukemia, tissue damage, etc. Old conventional method used in the hospital laboratories involves manual counting of blood cells using device called Hemocytometer and microscope. But this method extremely monotonous, laborious, time consuming, and leads to the inaccurate results due to human errors. Also there are some expensive machines like Analyzer, which are not affordable by every laboratories. The objective of this paper is to produce a survey on an image processing based system that can automatically detect and count the number of RBCs and WBCs in the blood sample image. Image Acquisition, Pre-Processing, Image Enhancement, Image Segmentation, Image Post-Processing and Counting algorithm these are six steps involved in an image processing algorithm. The objective of this research is to study the different methodologies of cells counting.

International Journal of Engineering Research and, 2020

Analysis of blood smear is an important diagnostic test used in the diagnosis of an array of diseases. The method of automatic diagnosis of microscopic blood smear images by identifying and separating it to different categories of cells that are highlighted in the paper. Automation of this process ultimately narrows the scope of possible diseases saving a considerable amount of time. Identification of red blood cells (RBCs) is carried out/done by the system using different techniques of image processing operations like pre-processing, operations for morphology, labelling and extraction of features to calculate shape and size of the RBCs. Morphological properties can provide information regarding shape of the cell. By these operations and calculations, RBCs are classified. There are 2 stages in red cell classification process, first is the separation of RBCs to normal and abnormal followed by abnormal cells classification to three subclasses based on the cell shape and structure. The aim of this system is to assist pathologist by giving quick results by analysing the smear samples. The cure of these diseases is possible, when it is detected at an earlier stage.

Image processing techniques are widely used in the domain of medical sciences for detecting various diseases, infections, tumours, cell abnormalities and various cancers. Detecting and curing a disease on time is very important in the field of medicine for protecting and saving human life. Mostly in case of high severity diseases where the mortality rates are more, the waiting time of patients for their reports such as blood test, MRI is more. The time taken for generation of any of the test is from 1-3 days. The current system used by the pathologists for identification of blood parameters is costly and the time involved in generation of the reports is also more sometimes leading to loss of patient's life. Also the pathological tests are expensive, which are sometimes not affordable by the patient. This paper deals with an image processing technique used for detecting the abnormalities of blood cells in less time. The proposed technique also helps in identifying the dead cells in the blood samples using morphological techniques through cell opening and closing which gives upto 89% computational accuracy.

2020

At present, the process of separating normal and abnormal red blood cells was carried out through laboratory testing, which often requires a lot of time and cost. Therefore, this paper presents an abnormal red blood cell detection scheme to make it easier to diagnose a disease, speed up the identification process so that it saves time and money. Because without having to go through a chemical process, which processes one by one so that it slows down the time of identification and uses high costs, the method proposed in this paper was to calculate the number of normal and abnormal blood cell objects using the digital image processing method using Matlab. This study could not only determine normal and abnormal blood cells using a feature extraction method based on shape. The impact of this study was to create a system to separate blood cells that have collided before the process of detecting the form and calculation of the number of normal and abnormal red blood cell objects in an image of a blood cell.

IAEME PUBLICATION, 2019

The measure of blood cells of red blood and white blood plays a important role for the diagnostic to analyze and look at various illness such as anemia, leukemia etc. Accurate counting of blood cells plays a very important role in diagnosing diseases. Exiting methods took more time for processing and produced in accurate result. Hardware device such as Hematology counteris very expensive and every hospital can’t af ord it. In order to overcome these problems, we are creating an android application with OpenCV libraries in Android Studio for a mobile device, the device can be connected to a microscopic camera via USB to take an image of the blood sample, this blood sample islater analyzed by implementing image processing approach of the blood using circular Hough transform and thresholding techniques. These separates the RBC and WBC by marking them, so that the algorithm can easily, detect and the counting of blood cells will be done on two microscopic blood images of each patient which will result in nearly accurate results than the present available methods. Accuracy and time consumption can be improved by using the proposed method of well-structured circle detection algorithm.

Indonesian Journal of Electrical Engineering and Computer Science

In medical field, the recognition of red blood cells (RBC) is used as an indicator to detect the type of diseases such as anaemia, malaria and leukaemia etc. The problems using manual detection of normal and abnormal RBCs under the microscope is tend to give inaccurate result and errors. This paper proposed a method to recognize the normal and abnormal shaped RBCs image by using Form Factor, Perimeter and Area as feature descriptors. Detecting normal cells of RBCs indicate a healthy patient and abnormal cells indicate presence of disease. And is very important in medical field to detect abnormal condition in early stage because it saves and protects human lives. The patients waiting time for blood test is more because the time taking to generate the result of the patient is high due to high demand and less equipment this method is used in order to improve the accuracy of the existing one and 94% accuracy was achieved in the detection.