4.5 Article

Multi-threshold image segmentation for melanoma based on Kapur's entropy using enhanced ant colony optimization

Journal

FRONTIERS IN NEUROINFORMATICS
Volume 16, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2022.1041799

Keywords

melanoma; multi-threshold image segmentation; Kapur's entropy; swarm intelligence; ant colony algorithm

Funding

  1. Wenzhou Scientific and Technological Project [Y20180656]
  2. Natural Science Foundation of Jilin Provincial [20200201053JC]
  3. Thirteenth Five-Year Science and Technology Project of Jilin Provincial Department of Education [JJKH20200829KJ]
  4. Changchun Normal University Ph.D. Research Startup Funding Project
  5. National Natural Science Foundation of China [62076185, U1809209]

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This study proposes a new multi-threshold image segmentation model based on the two-dimensional histogram approach, using an enhanced ant colony optimization algorithm combined with two-dimensional Kapur's entropy to search for optimal thresholds. Experimental results demonstrate that the proposed model outperforms the comparison method in segmenting images and provides high-quality samples for subsequent analysis of melanoma pathology images.
Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur's entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.

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