Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images

A follow-up blood count was performed on a 74-year-old woman diagnosed with colitis due to cytomegalovirus and under treatment with valganciclovir. The automated complete blood count revealed an abnormal white blood cells (WBC) scattergram together with WBC alert flags. The peripheral blood smear showed neutrophils with markedly hyposegmented nuclei or bilobed nuclei and very condensed chromatin or clumping chromatin all consistent with Pelger-Huët anomaly (PHA). We checked previous blood counts, ruling out an inherited PHA. We assessed the haematological, infectious and iatrogenic aetiologies for an acquired PHA. Once the valganciclovir treatment was completed and the drug was withdrawn, without changing the rest of the treatment, the morphological abnormalities of neutrophils were completely resolved. We conclude therefore that the acquired PHA presented by our patient is probably related to valganciclovir treatment.

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PENENTUAN KRITERIA PENILAIAN KESAN JUMLAH LEUKOSIT PADA PEMERIKSAAN APUSAN DARAH TEPI

Jurnal Kesehatan Panrita Husada ◽

10.37362/jkph.v3i2.158 ◽

2018 ◽

Vol 3 (2) ◽

pp. 52-61

Author(s):

Dzikra Arwie ◽

Islawati

Keyword(s):

Data Analysis ◽

Statistical Analysis ◽

Blood Cells ◽

Blood Smear ◽

White Blood Cells ◽

Peripheral Blood Smear ◽

Field Of View ◽

Laboratory Observation ◽

Number Of Cells

Leukocytes or white blood cells have a characteristic characteristic of different cells. Determination of the impression of the number of leukocytes is determined in the number of cells in the field of view. While the number of viewable field cells expressed is still quite varied. The purpose of this study was to determine the number of leukocytes in the field of view and expressed the impression of a sufficient amount. This research was conducted at the Laboratory of Health Analyst Department Panrita Husada Bulukumba on 9 April 2017 to 14 July 2017. This type of research is a laboratory observation that aims to determine the criteria for assessing the impression of the number of leukocytes on a peripheral blood smear. Data analysis using statistical analysis is the average and standard deviations to determine the impression of the number of leukocytes and use 3 inspection zones. The results of this study obtained results in zone IV the number of leukocyte impressions said to be sufficient was 7-10, in zone V the number of leukocyte impressions said to be sufficient was 4-9, and in zone VI the number of leukocyte impressions said to be sufficient was 3-8.

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Blood Cell Count Using Deep Learning Semantic Segmentation

10.20944/preprints201909.0075.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Thanh Tran ◽

Lam Binh Minh ◽

Suk-Hwan Lee ◽

Ki-Ryong Kwon

Keyword(s):

Deep Learning ◽

Blood Cell ◽

Cell Count ◽

Blood Cells ◽

Blood Smear ◽

White Blood Cells ◽

Semantic Segmentation ◽

Peripheral Blood Smear ◽

Cell Counting ◽

Blood Cell Count

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

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Automatic classification of peripheral blood smear cells by the example of lead poisoning

Farmacja Polska ◽

10.32383/farmpol/125764 ◽

2020 ◽

Vol 76 (6) ◽

pp. 318-323

Author(s):

Adrian Michalski ◽

Bogumiła Kupcewicz

Keyword(s):

Lead Poisoning ◽

Peripheral Blood ◽

Blood Smear ◽

Peripheral Blood Smear ◽

Automatic Classification

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Convex Hull Based Detection of Overlapping Red Blood Cells in Peripheral Blood Smear Images

7th WACBE World Congress on Bioengineering 2015 - IFMBE Proceedings ◽

10.1007/978-3-319-19452-3_14 ◽

2015 ◽

pp. 51-53 ◽

Cited By ~ 2

Author(s):

Feminna Sheeba ◽

Robinson Thamburaj ◽

Joy John Mammen ◽

Mohan Kumar ◽

Vansant Rangslang

Keyword(s):

Convex Hull ◽

Red Blood Cells ◽

Peripheral Blood ◽

Blood Cells ◽

Blood Smear ◽

Peripheral Blood Smear

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Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/574184 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 13

Author(s):

Ramin Soltanzadeh ◽

Hossein Rabbani ◽

Ardeshir Talebi

Keyword(s):

Blood Cells ◽

Main Part ◽

Blood Smear ◽

Active Form ◽

White Blood Cells ◽

Curvelet Transform ◽

Peripheral Blood Smear ◽

Important Symptom ◽

Blast Cells ◽

Giant Nuclei

The main part of each white blood cell (WBC) is its nucleus which contains chromosomes. Although white blood cells (WBCs) with giant nuclei are the main symptom of leukemia, they are not sufficient to prove this disease and other symptoms must be investigated. For example another important symptom of leukemia is the existence of nucleolus in nucleus. The nucleus contains chromatin and a structure called the nucleolus. Chromatin is DNA in its active form while nucleolus is composed of protein and RNA, which are usually inactive. In this paper, to diagnose this symptom and in order to discriminate between nucleoli and chromatins, we employ curvelet transform, which is a multiresolution transform for detecting 2D singularities in images. For this reason, at first nuclei are extracted by means ofK-means method, then curvelet transform is applied on extracted nuclei and the coefficients are modified, and finally reconstructed image is used to extract the candidate locations of chromatins and nucleoli. This method is applied on 100 microscopic images and succeeds with specificity of 80.2% and sensitivity of 84.3% to detect the nucleolus candidate zone. After nucleolus candidate zone detection, new features that can be used to classify atypical and blast cells such as gradient of saturation channel are extracted.

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