A Review on Systematic Investigation of Leucocytes Identification and Classification Techniques for Microscopic Blood Smear

Processing Techniques ◽

Improved Technology

Healthcare services are an important part of human beings and healthcare services are changing with new and innovative technologies. In recent day’s healthcare sector performing very crucial role in metamorphose of traditional health services to e-health technologies. This proposal provides an error-free and improved technology-based blood analysis service for the identification of leucocytes in blood samples of humans. Leucocytes play a vital and important character in human immune systems. This system helps to protect the body from suffering from leukemia. Leukemia, a blood cancer, nowadays is commonly found in all age persons. Leukemia is a type of disease and image processing techniques and algorithms can play a crucial role in disease diagnostic methodology. Identification of leukocytes in blood smear provides important information to pathologist as well as doctors to analyze and predicts different types of diseases, such as cancer. However, this analysis is critical and major complexities which results in errors and also takes a lot of time for analysis. Most of the time, the laboratory practitioners and doctors are interested only in leucocytes in blood smear. Medical image processing techniques strongly supports in their critical diagnosis and better results.

Detection of diseases via blood analysis using Image processing Techniques

2018 International Conference on Smart City and Emerging Technology (ICSCET) ◽

10.1109/icscet.2018.8537364 ◽

2018 ◽

Author(s):

Kajal Jewani ◽

Krishna Boddu ◽

Pratik Gumani ◽

Kiran Solapure

Keyword(s):

Image Processing ◽

Blood Analysis ◽

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

International Journal of Clinical and Biomedical Research ◽

10.31878/ijcbr.2018.43.01 ◽

2018 ◽

Vol 4 (3) ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Asaad Babker ◽

Vyacheslav Lyashenko

Keyword(s):

Image Processing ◽

Image Analysis ◽

Blood Smear ◽

Color Image ◽

Megaloblastic Anemia ◽

Cell Structure ◽

Color Space ◽

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.

Handbook of Research on Deep Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

Detection of Blood-Related Diseases Using Deep Neural Nets

10.4018/978-1-5225-7862-8.ch001 ◽

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 ◽

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.

Specifics of Using Image Processing Techniques for Blood Smear Analysis

Journal La Medihealtico ◽

10.37899/journallamedihealtico.v1i6.162 ◽

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 ◽

Important Stage ◽

Blood Smears ◽

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.

Identification of Tumour from Ct Images using Contour Plot and Gui

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8106.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 4306-4309

Keyword(s):

Image Processing ◽

Healthcare Sector ◽

Contour Plot ◽

Age Group ◽

Basic Image ◽

Processing Techniques ◽

Human Eyes ◽

Different Levels

The healthcare sector in terms of medical imaging is picking up significance with people preferring automation which is eventually fast and effective in determination of the problem which can give understanding to the picture way superior than to the human eyes. Brain tumour is a condition where it ranks second in terms of cancer related deaths for men and ranks at fifth place for women over the age group of 20 to 39.Brain tumours are extremely agonizing and it ends up being significant reasons for various ailments if not cured properly. Analysis of the tumour and its type is a very significant part in its treatment. Tumours are of two types benign and malignant, Distinguishing the type of tumour place an important role in its treatment .The principal reason for the rise in the number of malignancy patients is due to numbness towards its treatment at early stages. The whole idea of this paper is to create an algorithm that could educate the patient about the tumour with the help of image processing techniques. The basic image processing techniques are used to obtain the background by the sharpening the image, reduction of noise together with morphological functions such as erosion and dilation. To obtain the tumour images we are intended to subtract the background of the image and their negatives from the various set of images. Plotting contour and c-label of the tumour and its boundary provides us with information related to the tumour that can help in a better visualization in diagnosing cases. This procedure helps in recognizing the size, shape and location of the tumour. This in turn helps the doctors as well as the patient to comprehend the complexity of tumour with colour labelling for different levels of elevation. A graphical user interface would help the medicinal staff to access the reports and also find the background and contour plot of tumour within their finger tips.

Detection of Malarial Parasites using Image Processing Techniques from Blood Smear Slides

Research Journal of Pharmacy and Technology ◽

10.5958/0974-360x.2018.00805.3 ◽

2018 ◽

Vol 11 (10) ◽

pp. 4401 ◽

Cited By ~ 1

Author(s):

B. Padmapriya ◽

M. S. Sangeetha ◽

G. Ramya Priya Nandhini ◽

T. T. Anusha Devi

Keyword(s):

Image Processing ◽

Blood Smear ◽

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.