Literature DB >> 25041828

Automated microscopic image analysis for leukocytes identification: a survey.

Mukesh Saraswat1, K V Arya2.   

Abstract

Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification. However, there are several related problems viz., coarse background, overlapped nuclei, conversion of 3-D nuclei into 2-D nuclei etc. In this review, we have categorized, evaluated, and discussed recently developed methods for leukocyte identification. After reviewing these methods and finding their constraints, a future research perspective has been presented. Further, the challenges faced by the pathologists with respect to these problems are also discussed.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  Classification; Feature extraction; Feature selection; Image preprocessing; Leukocytes segmentation

Mesh:

Year:  2014        PMID: 25041828     DOI: 10.1016/j.micron.2014.04.001

Source DB:  PubMed          Journal:  Micron        ISSN: 0968-4328            Impact factor:   2.251


  11 in total

1.  An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images.

Authors:  Zeinab Moshavash; Habibollah Danyali; Mohammad Sadegh Helfroush
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

2.  Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.

Authors:  Keigo Kono; Ruka Hayata; Satoru Murakami; Mai Yamamoto; Maiko Kuroki; Kana Nanato; Kazuto Takahashi; Keiko Miwa; Yutaka Tsutsumi; Kazunori Okada; Sanae Kaga; Taisei Mikami; Nobuo Masauzi
Journal:  J Clin Lab Anal       Date:  2017-02-21       Impact factor: 2.352

3.  Feature selection and classification of leukocytes using random forest.

Authors:  Mukesh Saraswat; K V Arya
Journal:  Med Biol Eng Comput       Date:  2014-10-05       Impact factor: 2.602

4.  Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

Authors:  Enas Abdulhay; Mazin Abed Mohammed; Dheyaa Ahmed Ibrahim; N Arunkumar; V Venkatraman
Journal:  J Med Syst       Date:  2018-02-17       Impact factor: 4.460

5.  White blood cell segmentation by color-space-based k-means clustering.

Authors:  Congcong Zhang; Xiaoyan Xiao; Xiaomei Li; Ying-Jie Chen; Wu Zhen; Jun Chang; Chengyun Zheng; Zhi Liu
Journal:  Sensors (Basel)       Date:  2014-09-01       Impact factor: 3.576

6.  Histological image segmentation using fast mean shift clustering method.

Authors:  Geming Wu; Xinyan Zhao; Shuqian Luo; Hongli Shi
Journal:  Biomed Eng Online       Date:  2015-03-20       Impact factor: 2.819

7.  Touching Soma Segmentation Based on the Rayburst Sampling Algorithm.

Authors:  Tianyu Hu; Qiufeng Xu; Wei Lv; Qian Liu
Journal:  Neuroinformatics       Date:  2017-10

8.  Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method.

Authors:  Yan Li; Rui Zhu; Lei Mi; Yihui Cao; Di Yao
Journal:  Comput Math Methods Med       Date:  2016-05-22       Impact factor: 2.238

9.  The Best Texture Features for Leukocytes Recognition.

Authors:  Omid Sarrafzadeh; Alireza M Dehnavi; Hossein Y Banaem; Ardeshir Talebi; Arshin Gharibi
Journal:  J Med Signals Sens       Date:  2017 Oct-Dec

10.  Assessment of dysplasia in bone marrow smear with convolutional neural network.

Authors:  Jinichi Mori; Shizuo Kaji; Hiroki Kawai; Satoshi Kida; Masaharu Tsubokura; Masahiko Fukatsu; Kayo Harada; Hideyoshi Noji; Takayuki Ikezoe; Tomoya Maeda; Akira Matsuda
Journal:  Sci Rep       Date:  2020-09-07       Impact factor: 4.379

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