White Blood Cell Segmentation and Classification Using Deep Learning Coupled with Image Processing Technique

2021 ◽  
pp. 399-410
Author(s):  
Hieu Trung Huynh ◽  
Vo Vuong Thanh Dat ◽  
Ha Bao Anh
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Blood Cell ◽  
White Blood Cell ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Cell Segmentation

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. 

scholarly journals A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

2009 ◽  
Vol 11 (1) ◽  
pp. 196-206 ◽  
Author(s):  
Farnoosh Sadeghian ◽  
Zainina Seman ◽  
Abdul Rahman Ramli ◽  
Badrul Hisham Abdul Kahar ◽  
M-Iqbal Saripan
Keyword(s):  
Image Processing ◽  
Blood Cell ◽  
Digital Image Processing ◽  
White Blood Cell ◽  
Digital Image ◽  
Cell Segmentation

scholarly journals Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease

2017 ◽  
Vol 107 ◽  
pp. 85-104
Author(s):  
Raju Anitha ◽  
S. Jyothi ◽  
Venkata Naresh Mandhala ◽  
Debnath Bhattacharyya ◽  
Tai-hoon Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection ◽  
Processing Technique ◽  
Image Processing Technique

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.

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