A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images
- 30 April 2021
- journal article
- research article
- Published by International Information and Engineering Technology Association in Traitement du Signal
- Vol. 38 (2), 309-314
- https://doi.org/10.18280/ts.380207
Abstract
Accurate medical images segmentation plays a vital role in contouring during diagnosis and treatment planning. To improve the segmentation accuracy in low contrast images, we propose a method by combining Hill entropy and fuzzy c-partition. Here, using membership function, an image is first transformed into fuzzy domain. Subsequently, the fuzzy Hill entropies are defined for foreground (object) and background. Next, the total fuzzy Hill entropy is maximized to compute the accurate threshold; this process is employed to calculate a proper parameter combination of membership function. This Hill entropy is then optimized to acquire an image threshold by Differential Evolution "DE" optimization algorithm. The key benefit of the presented approach is that it considers the information of background and object as well as interactions between them in threshold selection mechanism. The results and performance evaluations show the better accuracy of our technique over other existing approaches.Keywords
This publication has 13 references indexed in Scilit:
- Motion detection using block based bi-directional optical flow methodJournal of Visual Communication and Image Representation, 2017
- Detection of moving objects based on enhancement of optical flowOptik, 2017
- A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detectionScientia Iranica, 2017
- The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image SegmentationJournal of Intelligent Systems, 2017
- Moving object area detection using normalized self adaptive optical flowOptik, 2016
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015
- A new 2D histogram scheme for colour image segmentationThe Imaging Science Journal, 2009
- Object segmentation using ant colony optimization algorithm and fuzzy entropyPattern Recognition Letters, 2007
- Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithmPattern Recognition Letters, 2003