scholarly journals Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

2020 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Zhana Fidakar Mohammed ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Processing ◽  
Information Hiding ◽  
Blood Cells ◽  
Medical Image ◽  
White Blood Cells ◽  
Segmentation Technique ◽  
Research Areas ◽  
Global Consistency ◽  
Random Index ◽  
Consistency Error

Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

scholarly journals A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis

2018 ◽  
Vol 12 (3) ◽  
pp. 56 ◽  
Author(s):  
Hussam N. Fakhouri ◽  
Saleh H. Al-Sharaeh
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
White Blood Cells ◽  
Medical Image Processing ◽  
Initial Diagnosis ◽  
Feature Analysis ◽  
Microscopic Images ◽  
Different Types ◽  
The Status ◽  
Blast Cells

Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.

scholarly journals Segmentation of White Blood Cells and Lymphoblast Cells Using Moving K-Means

2018 ◽  
Vol 8 (2) ◽  
pp. 211
Author(s):  
Ika Candradewi ◽  
Reno Ghaffur Bagasjvara
Keyword(s):  
Image Processing ◽  
Acute Lymphoblastic Leukemia ◽  
Digital Image Processing ◽  
Digital Image ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Image Processing Technique ◽  
Cell Segmentation ◽  
Lymphoblast Cells

One of the diagnosis procedures for acute lymphoblastic leukemia is screening for blood cells by expert operator using microscope. This process is relatively long and will slow healing process of this disease which need fast treatment. Another way to screen this disease is by using digital image processing technique in microscopic image of blood smears to detect lymphoblast cells and types of white blood cells. One of essential step in digital image processing is segmentation because this process influences the subsequent process of detecting and classifying Acute Lymphoblastic Leukemia disease. This research performed segmentation of white blood cells using moving k-means algorithm. Some process are done to remove noise such as red blood cells and reduce detection errors such as white blood cells and/or lymphoblastic cell  that’s appear overlap. Postprocessing are performed to improve segmentation quality and to separate connected white blood cell. The dataset in this study has been validated with expert clinical pathologists from Sardjito Regional General Hospital, Yogyakarta, Indonesia. This research produces systems performance with results in sensitivity of 85.6%, precision 82.3%, Fscore of 83,9% and accuracy of 72.3%. Based on the results of the testing process with a much larger number of datasets on the side of the variations level of cell segmentation difficulties both in terms of illumination and overlapping cell, the method proposed in this study was able to detect or segment overlapping white blood cells better.

scholarly journals Classification of texture using random box counting and binarization methods

2021 ◽  
Vol 10 (1) ◽  
pp. 533-540
Author(s):  
Wijdan Jaber AL-kubaisy ◽  
Maha Mahmood
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Blood Cells ◽  
Multifractal Analysis ◽  
Vision System ◽  
White Blood Cells ◽  
Real Life ◽  
Self Similarity ◽  
Box Counting

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

scholarly journals A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing

2021 ◽  
Vol 11 (6) ◽  
pp. 5060
Author(s):  
Mohammed Al-Momin ◽  
Ammar Almomin
Keyword(s):  
Image Processing ◽  
Blood Cells ◽  
White Blood Cells ◽  
Problem Of Time ◽  
Blood Constituents ◽  
Sickle Cells ◽  
Blood Disorder ◽  
Blood Smears ◽  
High Level ◽  
Processing Techniques

<span lang="EN-US">The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy.</span>

scholarly journals Red blood cells and white blood cells detection by image processing

2020 ◽  
Vol 1539 ◽  
pp. 012025
Author(s):  
Irwan Rahadi ◽  
Meechoke Choodoung ◽  
Arunsri Choodoung
Keyword(s):  
Image Processing ◽  
Red Blood Cells ◽  
Blood Cells ◽  
White Blood Cells

scholarly journals An efficient technique for identification of leukemia in microscopic blood samples using image processing.

2019 ◽  
Vol 10 (3) ◽  
pp. 2409-2416 ◽  
Author(s):  
Meghana M.R ◽  
Akshatha Prabhu
Keyword(s):  
Image Processing ◽  
Bone Marrow ◽  
Image Segmentation ◽  
Blood Cells ◽  
White Blood Cells ◽  
Efficient Technique ◽  
Blood Samples ◽  
Blood Cancer ◽  
The Way

Leukemia is a blood cancer which features through the ejection of manipulated and strange fabrication of white blood cells which is the way of bone marrow within the blood. The project aims at designing and developing an efficient technique for the detection of luekemia based on image segmentation techniques and nuclei analysis which incorporates the affected percentage and are compared and classified using KNN and SVM. The DNA of youngster cells, for the maximum detail white platelets, subsequently finally ends up harmed here and there. This version from the norm reasons platelets to increase and separate constantly. Sound platelets bypass on inevitably and are supplanted by approach of new cells, which might be brought in bone marrow. 

scholarly journals Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)

2016 ◽  
Vol 6 (2) ◽  
pp. 151 ◽  
Author(s):  
Mizan Nur Khasanah ◽  
Agus Harjoko ◽  
Ika Candradewi
Keyword(s):  
Image Processing ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
White Blood Cells ◽  
Processing Technique ◽  
Image Processing Technique ◽  
K Nearest Neighbor ◽  
Traditional Procedure ◽  
Early Diagnose

The traditional procedure of classification of blood cells using a microscope in the laboratory of hematology to obtain information types of blood cells. It has become a cornerstone in the laboratory of hematology to diagnose and monitor hematologic disorders. However, the manual procedure through a series of labory test can take a while. Thresfore, this research can be helpful in the early stages of the classification of white blood cells automatically in the medical field.Efforts to overcome the length of time and for the purposes of early diagnose can use the image processing technique based on morphology of blood cells. This research aims to classify the white blood cells based on cell morphology with the k-nearest neighbor (knn). Image processing algorithms used hough circle, thresholding, feature extraction, then to the process of classification was used the method of k-nearest neighbor (knn).In the process of testing used 100 images to be aware of its kind. The test results showed segmentation accuracy of 78% and testing the classification of 64%.

scholarly journals Comparative Analysis of White Blood Cell Cancer Detection Using Image Processing and IoT

2021 ◽  
pp. 198-201
Author(s):  
Dr. T. Loganayagi ◽  
Sindhu. K ◽  
Sripriyadharshini. S
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Blood Cell ◽  
Cancer Detection ◽  
Blood Cells ◽  
Cell Cancer ◽  
White Blood Cells ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
New Applications

Computerized analysis of white blood cells tumor such as Leukemia and Myeloma is an essential testing biomedical investigate point. Herein, a comparative analysis of image processing algorithms to detect the cancer is made and patient’s health is monitored using IOT also analyzed. The work could be useful for developing and exploring the new applications of image processing in IOT based systems.

scholarly journals Deep Categorization of Blood Cells u sing Depthwise Convolutions

2021 ◽  
Vol 10 (12) ◽  
pp. 76-79
Author(s):  
T Sudarshan Rao ◽  
◽  
N Rohan Sai ◽  
D Koteswara Rao ◽  
◽  
...  
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Cost Reduction ◽  
Medical Image ◽  
Intelligent System ◽  
White Blood Cells ◽  
Cell Types ◽  
Stringent Requirement ◽  
Cell Groups

Modern-day computation has become indispensable in the healthcare industry. From medical image processing to cost reduction, Artificial Intelligence has proved its significance in solving complex healthcare problems. One of the primary areas in which it can be of greater use in hematology. Categorization of white-blood cells is imperative to pre-identify abnormalities. Through this paper, we collected image samples for 4 major White Blood cell groups, which are Neutrophils, Lymphocytes, Monocytes, and Eosinophils. The aim of this research is to put forward an intelligent system that efficiently alleviates the stringent requirement of a cytological study. The proposed system classifies 4 white-blood-cell types based on their morphological variation. With the experimental modulations that we chose to integrate, the presented model attained an accuracy of 97%.

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