scholarly journals Specifics of Using Image Processing Techniques for Blood Smear Analysis

2020 ◽  
Vol 1 (6) ◽  
pp. 1-6
Author(s):  
Vyacheslav Lyashenko ◽  
Tetiana Sinelnikova ◽  
Oleksandr Zeleniy ◽  
Asaad Mohammed Ahmed Babker
Keyword(s):  
Image Processing ◽  
Human Health ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Important Stage ◽  
Blood Smears ◽  
Processing Techniques

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

scholarly journals Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

2020 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Dr. Dhaya R.
Keyword(s):  
Image Processing ◽  
Virtual Reality ◽  
Processing Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Processing Techniques

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

2021 ◽  
pp. 103-115
Author(s):  
Eimad Abdu Abusham
Keyword(s):  
Image Processing ◽  
Traditional Method ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Economic Losses ◽  
Image Processing Techniques ◽  
Appropriate Treatment ◽  
Processing Techniques

Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.

scholarly journals Application of Neuro-Wavelet Algorithm in Ultrasonic-Phased Array Nondestructive Testing of Polyethylene Pipelines

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Reza Bohlouli ◽  
Babak Rostami ◽  
Jafar Keighobadi
Keyword(s):  
Image Processing ◽  
Nondestructive Testing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Phased Array ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Ultrasonic Phased Array ◽  
Processing Techniques

Polyethylene (PE) pipelines with electrofusion (EF) joining is an essential method of transportation of gas energy. EF joints are weak points for leakage and therefore, Nondestructive testing (NDT) methods including ultrasonic array technology are necessary. This paper presents a practical NDT method of fusion joints of polyethylene piping using intelligent ultrasonic image processing techniques. In the proposed method, to detect the defects of electrofusion joints, the NDT is applied based on an ANN-Wavelet method as a digital image processing technique. The proposed approach includes four steps. First an ultrasonic-phased array technique is used to provide real time images of high resolution. In the second step, the images are preprocessed by digital image processing techniques for noise reduction and detection of ROI (Region of Interest). Furthermore, to make more improvement on the images, mathematical morphology techniques such as dilation and erosion are applied. In the 3rd step, a wavelet transform is used to develop a feature vector containing 3-dimensional information on various types of defects. In the final step, all the feature vectors are classified through a backpropagation-based ANN algorithm. The obtained results show that the proposed algorithms are highly reliable and also precise for NDT monitoring.

scholarly journals Image Processing for Technological Diagnostics of Metallurgical Facilities

2012 ◽  
Vol 12 (4) ◽  
pp. 66-76 ◽  
Author(s):  
Lyubka Doukovska ◽  
Venko Petkov ◽  
Emil Mihailov ◽  
Svetla Vassileva
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Digital Processing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
One Dimensional ◽  
Image Processing Techniques ◽  
Digital Image Processing Technique ◽  
Multidimensional Signals ◽  
Processing Techniques

Abstract The paper presents an overview of the image-processing techniques. The set of basic theoretical instruments includes methods of mathematical analysis, linear algebra, probability theory and mathematical statistics, theory of digital processing of one-dimensional and multidimensional signals, wavelet-transforms and theory of information. This paper describes a methodology that aims to detect and diagnose faults, using thermographs approaches for the digital image processing technique.

Image Processing Techniques on Radiological Images of Human Lungs Effected by COVID-19

2020 ◽  
Vol 4 (2) ◽  
pp. 69
Author(s):  
A.M. Sirisha ◽  
P. Venkateswararao
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Wide Spread ◽  
Image Processing Techniques ◽  
Human Lungs ◽  
Significant Difference ◽  
Radiological Images ◽  
Processing Techniques ◽  
Healthy Lung

The wide spread of COVID-19 all over the world inspires every human to know and visualize its effect on human body. As   COVID-19 effects the human lungs here a number of radiological images of human lungs are analysed using an image processing technique called Threshold Segmentation. A significant difference is observed between healthy lung images and COVID-19 effected lung images.

Garbage Carrier Roboboat Based On Image Processing

2019 ◽  
Vol 7 (1) ◽  
pp. 25-41
Author(s):  
Ketut Abimanyu ◽  
Saeful Rohman ◽  
Asgia Setya ◽  
Pradito Octa
Keyword(s):  
Image Processing ◽  
Navigation System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Motor Speed ◽  
Working Process ◽  
Brushless Motors ◽  
Image Processing Techniques ◽  
Brushless Motor ◽  
Processing Techniques

This research explain about garbage carrier roboboat. the purpose of making this robot is to reduce the trash found on river surfaces. Indonesia is an archipelagic country, this country dominated by ocean as rives estuary, however many of the resources produced from the sea have been polluted due to dirty and full of garbage from the river. So with the creation of this robot, it is expected to minimize the pollution. The shape of this robot is a boat that can operate in rivers and waters that still allows this robot to operate. This robot uses a brushless motor to move on the surface of the water and electronic speed control to control the brusless motor speed with the PWM setting method, for the navigation system the HC5409 ultrasonic sensor is used to detect any obstacles that avoid collisions, then image processing techniques are applied to detect garbage on the surface of the water, The Matlab R2012a program is an application used to process this image processing technique. In sum, the working process of this boat robot is, first, the navigating robot looks for garbage objects by detecting it using image processing. If garbage is detected, the robot will maneuver and activate brushless motors with garbage manuever algorithms, instructions sent from the microcontroller to Arduino Mega 2560, the robot approaches garbage object and crawl it. There are two garbage objects determined, namely styrefoam and a blue bottle, this boat can execute advanced, right and left maneuvers with the percentage of success reaching 80-90%. The advantage of this boat is that garbage objects can be detected from various directions and operate semi-automatically . Keywords :MATLAB,  Image processing ,Motor brushless,  Arduino Mega 2560 , manuever .

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

MATLAB Image Processing as a Viable Tool to Study Low Surface Roughness

2015 ◽  
Author(s):  
Wesley S. Hunko ◽  
Vishnuvardhan Chandrasekaran ◽  
Lewis N. Payton
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Standard Deviation ◽  
Data Acquisition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Polished Surface ◽  
Digital Microscope ◽  
A New Technique ◽  
Processing Techniques

The purpose of this paper is to present the results of a study comparing an old technique for measuring low surface roughness with a new technique of data acquisition and processing that is potentially cheaper, quicker and more automated. It offers the promise of in-process quality monitoring of surface finish. Since the late 1800s, researchers have investigated the light scattering effects of surface asperities and have developed many interferometry techniques to quantify this phenomenon. Through the use of interferometry, the surface roughness of objects can be very accurately measured and compared. Unlike contact measurement such as profilometers, interferometry is nonintrusive and can take surface measurements at very wide ranges of scale. The drawbacks to this method are the high costs and complexity of data acquisition and analysis equipment. This study attempts to eliminate these drawbacks by developing a single built-in MATLAB function, to simplify data analysis, and a very economically priced digital microscope (less than $200), for data acquisition. This is done by comparing the results of various polishing compounds on the basis of the polished surface results obtained from MATLAB’s IMHIST function to the results of stylus profilometry methods. The study with the MATLAB method is also to be compared to 3D microscopy with a Keyence microscope. With surface roughness being a key component in many manufacturing and tribology applications, the apparent need for accurate, reliable and economical measuring systems is prevalent. However, interferometry is not a cheap or simple process. “Over the last few years, advances in image processing techniques have provided a basis for developing image-based surface roughness measuring techniques” [1]. One popular image processing technique is through the use of MATLAB’s Image Processing Toolbox. This includes an array of functions that can be used to quantify and compare textures of a surface. Some of these include standard deviation, entropy, and histograms of images for further analysis. “These statistics can characterize the texture of an image because they provide information about the local variability of the intensity values of pixels in an image. For example, in areas with smooth texture, the range of values in the neighborhood around a pixel will be a small value; in areas of rough texture, the range will be larger. Similarly, calculating the standard deviation of pixels in a neighborhood can indicate the degree of variability of pixel values in that region” [2]. By combining the practices of interferometry with the processing techniques of MATLAB, this fairly new method of roughness measurement proved itself as a very viable and inexpensive technique. This technique should prove to be a very viable means of interferometry at an affordable cost.

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