IMAGE SEGMENTATION TECHNIQUES FOR RED BLOOD CELL : ON OVERVIEW

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Laghouiter Oussama ◽  
M. Mahadi Abdul Jamil ◽  
Wan Mahani Hafiza Bt. Wan Mahmud
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Blood Cell ◽  
Blood Cells ◽  
Medical Image Analysis ◽  
White Blood Cells ◽  
Critical Factor ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Common

Image processing technique applies in different domains, such as medical, remote sensing and security. This techniques Aims to get a simple image called -image processed- should retain maximum useful information. The sensitive step in image processing is segmentation of image. Segmentation is first stage in medical image analysis seeded to two categories supervised and unsupervised technique. Accuracy of this stage affects the whole system performance. This paper present some methods applied for blood cell image segmentation and compares previous studies of overlapping cell division method. The common goal about this area is accuracy of counting the number of red blood cells (RBC) or white blood cells (WBC), which decrease with effect of some diseases such as anemia and leukemia. And makes it a critical factor in patient treatments.

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 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.

Review on 2D and 3D MRI Image Segmentation Techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 150-160 ◽  
Author(s):  
S. Shirly ◽  
K. Ramesh
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Diagnostics ◽  
Processing Technique ◽  
Image Features ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Edge Based ◽  
Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

scholarly journals Beef Quality Identification Using Thresholding Method and Decision Tree Classification Based on Android Smartphone

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kusworo Adi ◽  
Sri Pujiyanto ◽  
Oky Dwi Nurhayati ◽  
Adi Pamungkas
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Decision Tree ◽  
Processing Technique ◽  
Automated System ◽  
Image Processing Technique ◽  
Marbling Score ◽  
Beef Quality ◽  
Thresholding Method ◽  
Acquisition Processes

Beef is one of the animal food products that have high nutrition because it contains carbohydrates, proteins, fats, vitamins, and minerals. Therefore, the quality of beef should be maintained so that consumers get good beef quality. Determination of beef quality is commonly conducted visually by comparing the actual beef and reference pictures of each beef class. This process presents weaknesses, as it is subjective in nature and takes a considerable amount of time. Therefore, an automated system based on image processing that is capable of determining beef quality is required. This research aims to develop an image segmentation method by processing digital images. The system designed consists of image acquisition processes with varied distance, resolution, and angle. Image segmentation is done to separate the images of fat and meat using the Otsu thresholding method. Classification was carried out using the decision tree algorithm and the best accuracies were obtained at 90% for training and 84% for testing. Once developed, this system is then embedded into the android programming. Results show that the image processing technique is capable of proper marbling score identification.

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 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 Estimation of Traffic Occupancy using Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 7291-7295
Author(s):  
M. U. Farooq ◽  
A. Ahmed ◽  
S. M. Khan ◽  
M. B. Nawaz
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Percentage Difference ◽  
Novel Approach ◽  
Removal Technique

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Segmentation of White Blood Cell Nucleus Using Active Contour

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Nurhanis Izzati Che Marzuki ◽  
Nasrul Humaimi Mahmood ◽  
Mohd Azhar Abdul Razak
Keyword(s):  
Image Processing ◽  
Blood Cell ◽  
White Blood Cell ◽  
Active Contour ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Contour Method ◽  
Automated Diagnosis ◽  
Parameter Values

Image processing comes with various techniques. It uses a series of framework to transform an input image into an output image. In recent times, image processing technique has been extensively used in medical area. In order to overcome the problems of manual diagnosis in identifying the morphology of blood cells, the automated diagnosis is often used. Manual diagnosis required the observation of blood sample by expert hematologist and pathologist. This method may suffer from the presence of non-standard precision of human visual inspection. Due to this problem, this paper focused on semi-automated diagnosis that used image processing technique to perform the segmentation of the nucleus in white blood cell (WBC). Several image processing techniques are used including the active contour method. The results obtained are based on the parameter values obtained from segmentation process. The parameter value is calculated from the roundness equation. The value of 0.80 can be used to describe as a single leukocyte. 

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