Proceedings ArticleDOI
Blood Cells Counting using Python OpenCV
TLDR
The proposed system can benchmark with the manual methods of detection and counting of platelets, RBCs and WBCs in blood samples and was statistically described as accurate compared to the manual method of counting.Abstract:
Blood cells both white and red 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. 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. A cell counting method was also used to provide an actual count of the RBCs and WBCs 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.read more
Citations
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Proceedings ArticleDOI
Image-Processing-based Digital Goniometer using OpenCV
TL;DR: In this article, a digital goniometer that allows instantaneous measurement of the elbow and knee joint angles, through pictures, was created using Raspberry Pi, a microcomputer capable of supporting functional applications through integration of hardware and software components.
Proceedings ArticleDOI
Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification
Rogelio Ruzcko Tobias,Luigi Carlo De Jesus,Matt Ervin G. Mital,Sandy Lauguico,Marielet Guillermo,Ryan Rhay P. Vicerra,Argel A. Bandala,Elmer P. Dadios +7 more
TL;DR: A Faster Region-based Convolutional Neural Network (Faster R-CNN) was utilized for this study, focusing not only on RBCs but also on the variants of WBCs.
Journal ArticleDOI
Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.
TL;DR: In this paper, a deep learning-based method was proposed to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils.
Proceedings ArticleDOI
Manifold-Regularized Regression Network: A Novel End-to-End Method for Cell Counting and Localization
TL;DR: Experimental results show that MRRN outperforms several state-of-the-art algorithms on a widely-adopted synthetic data set and a real-world carcinoma data set, which verifies the efficacy and applicability of the proposed model.
References
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Proceedings ArticleDOI
Separation and counting of blood cells using geometrical features and distance transformed watershed
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Proceedings ArticleDOI
White blood cells nucleus segmentation from microscopic images of strained peripheral blood film during leukemia and normal condition
TL;DR: An algorithm to segment WBC nucleus from microscopic images of stained peripheral blood film during leukemia and normal condition is proposed and the accuracy of the result obtained is 88.57%.
Proceedings ArticleDOI
A Computer-Aided System for Differential Count from Peripheral Blood Cell Images
TL;DR: This research proposes a computer-aided systems that simulates a human visual inspection to automate the process of detection and identification of WBCs and RBCs from blood smear images and demonstrates a reliable and effective system for differential counting.