Detection and Implementation of Blood Group and Hb Level by Image Processing Techniques

Processing Techniques ◽

Clinical Experiments

In an emergency, an urgent blood transfusion from a person to the patient is required and blood group identification is the first process to do so. In addition, a hemoglobin test is often required to make decisions about blood transfusion as well as to check anemia. Hemoglobin testing is also required for complete blood count and monitoring a number of diseases. These blood tests are almost difficult in rural areas where lab facilities are not sufficient. Researchers proposed a number of methods to identify blood groups using computer vision techniques. However, no study was conducted to identify blood group and hemoglobin level in a work using image processing techniques and an android mobile application which shows high detection accuracy. In this paper, manual clinical experiments have been replaced by an android app using image processing techniques to detect blood groups and hemoglobin levels except users require using antigen before taking samples. The proposed technique is divided into two portions. The first portion is blood group detection, which is done by taking a blood sample and performing the grayscale conversion, binary conversion, segmentation, edge detection, and computation to make the decision. The second section describes how to determine hemoglobin levels by comparing a blood sample image to a hemoglobin color scale (HCS). Here, the Hemoglobin value is determined from their RGB values. It has been discovered that the proposed approaches are capable of detecting hemoglobin levels and blood groups in a cost-effective and error-free manner. As a result, the tests can be conducted in a remote area without adequate lab facilities and the proposed work can solve major steps in blood transfusion difficulties and anemia.

Blood Group Detection using Image Processing Techniques A Review

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i4.859863 ◽

2019 ◽

Vol 7 (4) ◽

pp. 859-863

Author(s):

Ruchi Jogi ◽

Avinash Dhole

Keyword(s):

Image Processing ◽

Blood Group ◽

Group Detection ◽

Improvement of Accuracy of Human Blood Groups Determination using Image processing Techniques

IJARCCE ◽

10.17148/ijarcce.2015.41090 ◽

2015 ◽

Vol 4 (10) ◽

pp. 411-412 ◽

Cited By ~ 1

Author(s):

Mehedi Hasan Talukder ◽

Md.Mahfuz Reza ◽

Mahbuba Begum ◽

Md. Rabiul Islam ◽

Md. Mahmudul Hasan

Keyword(s):

Image Processing ◽

Human Blood ◽

Blood Groups ◽

Blood Group Classification System Based on Image Processing Techniques

Intelligent Automation & Soft Computing ◽

10.32604/iasc.2022.019500 ◽

2022 ◽

Vol 31 (2) ◽

pp. 817-834

Author(s):

S. A. Shaban ◽

D. L. Elsheweikh

Keyword(s):

Image Processing ◽

Blood Group ◽

Classification System ◽

Group Classification ◽

AN EFFICIENT METHOD TO AUTOMATE POTHOLE DETECTION IN ROAD SURFACE USING IOT

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i10.025 ◽

2021 ◽

Vol 5 (10) ◽

Author(s):

Ajith Kumar B ◽

Vignesh G ◽

Anbumani A.

Keyword(s):

Image Processing ◽

Digital Image Processing ◽

Digital Image ◽

Road Surface ◽

Detection Accuracy ◽

Road Maintenance ◽

Application System ◽

Processing Techniques ◽

Pothole Detection

With the development of information technology, the digital image processing has the characteristics of strong permeability, large use of action and good comprehensive benefits. A road maintenance pothole detection is one of the important tasks. A road surface modelling or road image analysis is generally come from computer vision approaches. However, these two categories were always used independently. Furthermore, the accuracy of the pothole detection is not satisfactory. These challenges promote the development of a better application to detect potholes, cracks using the digital image processing like segmentation, extraction, recognition, and morphology from the images of road surface by using image processing. We are proposing an application system with efficient digital image processing techniques to improve the accuracy and consistency of obtaining accurate shapes of potholes and topologies, etc. The successful detection accuracy is around 98.7% and the overall pixel-level accuracy is approximately 99.6%. By using the digital image processing techniques, the detected potholes and cracks are updated to the web server by using IOT device.

RGB Image Cluster Evaluation for Human Blood Group Identification by MLP Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6005.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 4197-4202

Keyword(s):

Blood Sample ◽

Blood Group ◽

Median Filter ◽

Group Identification ◽

Optimal Solution ◽

Blood Groups ◽

Blood Type ◽

Blood Group System ◽

Group System ◽

Blood Exchange

Image processing is helping researchers to reach their goals in many ways, especially in medical fields. Blood organization is very important when it comes to receiving a blood exchange. The most important blood group identification method is ABO blood group system and the RhD blood group system. Blood groups are defined by the occupancy or preoccupied of a specific agglutinate on the get around of a red blood cell. Identifying the blood group is very important for medical treatment in pathological tests, at some point it gives us an inaccurate and also expensive result, therefore, to overcome these problems an efficient and optimal solution is required. The need for accurate detection is high in a disaster situation where there are no laboratory people or experts available to detect the type of it. In the proposed method, we have collected 50 blood sample images for each of 8 blood groups, total 400 blood sample images are considered for experimentation. In preprocessing, the median filter is used to eliminate noise from the blood images. Then these images are converted from RGB to grayscale conversion and also resizing of the images is carried out. Region based segmentation by using two methods Markov Random Field and Region Adjacency Graph are used for segmentation, texture, color, and shape features are extracted from segmented images. Hence this paper proposes a pixel cluster based analysis of the blood type based on the pixel analysis features. The overall accuracy of blood group determination is 93.85%.

Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.