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.
Highlights
Various computing techniques inspired from nature has been extensively used in solving problems spanning from optimization, pattern recognition, machine learning, image detection to computer vision
The plant growth simulation algorithm (PGSA) has been applied to solve this circle detection problem which gives the location of the white blood cell (WBC) in the images using three non-collinear edge points on the segmented edge map of the image as candidate circles
The resemblance of the encoded candidate circles to the actual WBC is evaluated by the objective function which uses the edge map and segmentation results for calculating the resemblances
Summary
Various computing techniques inspired from nature has been extensively used in solving problems spanning from optimization, pattern recognition, machine learning, image detection to computer vision. There is hardly any field that is left uninfluenced by the nature-based computing techniques. Image Processing is one such field where recently biomimicry methods are being used invariably. One such application of nature-inspired computation is in the field of medical image processing, especially focused on object localization. The problem of object localization in medical images has been solved with the application of the highly efficient plant growth simulation algorithm (PGSA) (Li and Wang 2008) applied to the analysis of white blood cell (WBC) images. WBCs, known as leukocytes, play a very important role in the diagnosis of a myriad
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