Chromatin Detection in Malaria Thick Blood Film Using Automated Image Processing

2015 ◽  
Vol 781 ◽  
pp. 616-619 ◽  
Author(s):  
Aeggarut Pinkaew ◽  
Tulaya Limpiti ◽  
Akraphon Trirat
Keyword(s):  
Image Processing ◽  
Blood Film ◽  
Color Model ◽  
Threshold Selection ◽  
Image Processing Algorithm ◽  
Thick Blood Film ◽  
Malaria Species ◽  
Hsi Color Model ◽  
Automated Image Processing ◽  
Global Health Problem

Malaria is a serious global health problem and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to aid the diagnosis of malaria on thick blood films is developed. Morphological and automatic threshold selection techniques are applied on two color components from the HSI color model to identify chromatins of P. Falciparum and P. Vivax malaria species on the images. Chromatins are positively identified with good sensitivities for both species. After identifying the position of chromatins, the algorithm splits the image into small sub-images, each with a chromatin in the center. These small images can subsequently be used by technician to classify malaria species more conveniently.

Research and Simulation on the Image Segmentation Algorithm with Corn Image Based on HSI Color Model

2012 ◽  
Vol 459 ◽  
pp. 128-131
Author(s):  
Xue Feng Hou ◽  
Yuan Yuan Shang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Algorithm ◽  
Color Model ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Hsi Color Model ◽  
Classical Image ◽  
Image Segmentation Algorithm

Image segmentation is one focus of digital image processing. In this paper, fourteen different kinds of classical image segmentation algorithms are studied and compared using corn image and simulating in MATLAB based on HSI color model. The result reveals that the method that using H component based on HSI color model to deal with the histogram threshold algorithm and Laplace edge detection algorithm is effectively extract the plant from the corn image

scholarly journals Improvement of Auto-Tracking Mobile Robot based on HSI Color Model

2018 ◽  
Vol 12 (3) ◽  
pp. 1349 ◽  
Author(s):  
Suresh Sundarajoo ◽  
Ahmad Shahrizan Abdul Ghani
Keyword(s):  
Mobile Robot ◽  
Light Level ◽  
Object Identification ◽  
Color Model ◽  
Vision Sensor ◽  
Image Processing Algorithm ◽  
Filtering Algorithm ◽  
Dc Motors ◽  
Hsi Color Model ◽  
Tracking Device

<p>Auto tracking mobile robot is a device that able to detect and track a target. For an auto tracking device, the most crucial part of the system is the object identification and tracking of the moving targets. In order to improve the accuracy of identification of object in different illumination and background conditions, the implementation of HSI color model is used in image processing algorithm. In this project HSI-based color filtering algorithm were used for object identification. This is because HSI parameter are more stable in different light and background conditions, so it is selected as the main parameters of this system. Pixy CMUcam5 is used as the vision sensor while Arduino Uno as the main microcontroller that controls all the input and output of the device. Besides that, L293D is used as the motor driver to control the movement of two DC motors that attached to the wheel of the robot. Moreover, two servo motors were used to control the pan-tilt movement of the vision sensor. Experimental results demonstrate that when HSI color-based filtering algorithm is applied to visual tracking it improves the accuracy and stability of tracking under the condition of varying brightness, or even in the low-light-level environment. Besides that, this algorithm also prevents tracking loss due to object color appears in the background.</p>

scholarly journals Quantifying biofilm propagation on chemically modified surfaces

2020 ◽  
Author(s):  
Michelle C. Halsted ◽  
Amber N. Bible ◽  
Jennifer L. Morrell-Falvey ◽  
Scott T. Retterer
Keyword(s):  
Image Processing ◽  
Bacterial Species ◽  
Processing Algorithm ◽  
Control Surface ◽  
Image Processing Algorithm ◽  
Surface Attachment ◽  
Hydrophilic Surfaces ◽  
Chemically Modified Surfaces ◽  
Automated Image Processing ◽  
Reduced Surface

AbstractConditions affecting biofilm formation differ among bacterial species and this presents a challenge to studying biofilms in the lab. This work leverages functionalized silanes to control surface chemistry in the study of early biofilm propagation, quantified with a semi-automated image processing algorithm. These methods support the study of Pantoea sp. YR343, a gram-negative bacterium isolated from the poplar rhizosphere. We found that Pantoea sp. YR343 does not readily attach to hydrophilic surfaces but will form biofilms with a “honeycomb” morphology on hydrophobic surfaces. Our image processing algorithm described here was used to quantify this honeycomb morphology over time and displayed a logarithmic behavior in the propagation of the honeycomb biofilm. This methodology was repeated with a flagella-deficient fliR mutant of Pantoea sp. YR343 which resulted in reduced surface attachment. Quantifiable differences between Pantoea WT and ΔfliR biofilm morphologies were captured by the image processing algorithm, further demonstrating the insight gained from these methods.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

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