scholarly journals Blood Group Detection using Image Processing MATLAB

2021 ◽  
Vol 9 (VI) ◽  
pp. 5006-5013
Author(s):  
P. Hansik Sagar
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Blood Group ◽  
Human Error ◽  
Healthcare Applications ◽  
Group Detection ◽  
Canny Edge ◽  
Time Test ◽  
Processing Techniques ◽  
Blood Grouping

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.

scholarly journals Endoscopic Video Pre-Processing using Histogram Equalization and Canny Edge Detection Technique and Hough Transform Technique for Polyp Detection

2019 ◽  
Vol 8 (4) ◽  
pp. 1793-1795
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Medical Practitioner ◽  
Histogram Equalization ◽  
Polyp Detection ◽  
Endoscopic Video ◽  
Canny Edge ◽  
Processing Techniques ◽  
Gastro Intestinal Tract ◽  
Endoscopic Videos

Paper An effective image processing approach has been shown to process real time endoscopic videos to support practitioners in taking vital decisions regarding cancer patients. The images are extracted from real time endoscopy videos using the available software and fed into Matlab for image processing, the results of the processed videos are returned back to the host software. Image processing techniques are used to recognize and enhance the visualization of the polyps present in gastro intestinal tract which help the practitioners in decision making. The intention of the system is to assist the physician or medical practitioner for better visualization and identifying abnormal structures like polyps and bleeding regions during the endoscopic procedures. This proposed system is experimented on the recorded gastrointestinal dataset which contains ten sequence videos with 7894 total frames.

scholarly journals Blood Group Detection using Matlab

2021 ◽  
Vol 9 (VI) ◽  
pp. 3089-3093
Author(s):  
Sujatha C. N
Keyword(s):  
Image Processing ◽  
Blood Group ◽  
Human Error ◽  
Group Identification ◽  
Group Testing ◽  
Emergency Situation ◽  
Blood Type ◽  
Morphological Operations ◽  
Laboratory Staff ◽  
Emergency Situations

Blood group testing is one of the vital tasks in the area of medicine, in which it is very important during emergency situation before victim requires blood transfusion. Presently, the blood tests are conducted manually by laboratory staff members, which is time consuming process in the emergency situations. Blood group identification within shortest possible time without any human error is an important factor and very much essential. Image processing paves a way in determining blood type without human intervention. Images which are captured using high resolution microscopic camera during the blood slide test in the laboratory which are used for blood type evaluation. The image processing techniques which include thresholding and morphological operations are used. The blood image is separated into sample wise and blood type is decided based on the agglutination effects in those sample images. This project facilitates the identification of blood group even by common people who are unaware of the blood typing procedure.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 327-347 ◽  
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

scholarly journals Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method

2021 ◽  
Vol 22 (3) ◽  
pp. 1796
Author(s):  
Satryo B. Utomo ◽  
Januar Fery Irawan ◽  
Rizqi Renafasih Alinra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Detection System ◽  
Water Levels ◽  
Level Gauge ◽  
Canny Edge Detection ◽  
Canny Edge

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.

scholarly journals Controlling Longitudinal Safe Distance Between Vehicles

2012 ◽  
Vol 21 (5) ◽  
pp. 303-310 ◽  
Author(s):  
Seyyed Mohammad Sadat Hoseini ◽  
Mahmmood Fathi ◽  
Manouchehr Vaziri
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Technology ◽  
Safe Distance ◽  
Main Concept ◽  
Average Speed ◽  
Image Processing Techniques ◽  
Online System ◽  
Police Agencies ◽  
Processing Techniques

Controlling the safe distances between vehicles on freeways can be used to prevent many accidents. In this research, image-processing techniques have been used to develop an online system that calculates the longitudinal distances between vehicles. This system facilitates controlling safe distances between vehicles without the need for high technology devices. Our approach is real-time and simple, but efficient operations have been used to reduce the image occlusion problem. The main concept of this system is using simple, quick, and effective algorithms for calculating the position of each vehicle in each image. In this way, traffic parameters like speed and distances between vehicles can be calculated for each vehicle in real time. In addition, aggregate parameters like average speed, density, and traffic flow can be calculated using gathered data of single vehicles. As an application of the developed system, controlling the safe distance between vehicles has been introduced. In this system, in case of a driver who does not observe the safe distance, the scene of violation is stored and can be used by the police agencies. KEY WORDS: image processing, traffic, longitudinal safe distance, real time, occlusion

Sign in / Sign up

Export Citation Format

Share Document