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An object localization optimization technique in medical images using plant growth simulation algorithm

Overview of attention for article published in SpringerPlus, October 2016
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1 tweeter

Citations

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Readers on

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17 Mendeley
Title
An object localization optimization technique in medical images using plant growth simulation algorithm
Published in
SpringerPlus, October 2016
DOI 10.1186/s40064-016-3444-2
Pubmed ID
Authors

Deblina Bhattacharjee, Anand Paul, Jeong Hong Kim, Mucheol Kim

Abstract

The analysis of leukocyte images has drawn interest from fields of both medicine and computer vision for quite some time where different techniques have been applied to automate the process of manual analysis and classification of such images. Manual analysis of blood samples to identify leukocytes is time-consuming and susceptible to error due to the different morphological features of the cells. In this article, the nature-inspired plant growth simulation algorithm has been applied to optimize the image processing technique of object localization of medical images of leukocytes. This paper presents a random bionic algorithm for the automated detection of white blood cells embedded in cluttered smear and stained images of blood samples that uses a fitness function that matches the resemblances of the generated candidate solution to an actual leukocyte. The set of candidate solutions evolves via successive iterations as the proposed algorithm proceeds, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The higher precision and sensitivity of the proposed scheme from the existing methods is validated with the experimental results of blood cell images. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 18%
Lecturer 2 12%
Student > Master 2 12%
Professor > Associate Professor 2 12%
Researcher 2 12%
Other 3 18%
Unknown 3 18%
Readers by discipline Count As %
Computer Science 7 41%
Engineering 2 12%
Nursing and Health Professions 1 6%
Chemical Engineering 1 6%
Agricultural and Biological Sciences 1 6%
Other 1 6%
Unknown 4 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 November 2016.
All research outputs
#18,482,034
of 22,901,818 outputs
Outputs from SpringerPlus
#1,261
of 1,850 outputs
Outputs of similar age
#241,704
of 319,489 outputs
Outputs of similar age from SpringerPlus
#98
of 136 outputs
Altmetric has tracked 22,901,818 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,850 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.