A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
On minimum error thresholding and its implementations
Pattern Recognition Letters
Multilevel thresholding using edge matching
Computer Vision, Graphics, and Image Processing
Fuzzy entropy threshold approach to breast cancer detection
Information Sciences—Applications: An International Journal
A fast iterative scheme for multilevel thresholding methods
Signal Processing
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Performance Evaluation of Thresholding Algorithms for Optical character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Expert Systems with Applications: An International Journal
Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
Digital image thresholding, based on topological stable-state
Pattern Recognition
IEEE Transactions on Multimedia
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
Multilevel image thresholding based on tsallis entropy and differential evolution
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Bacterial foraging based moon symmetry axis estimation for spacecraft attitude determination
International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
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Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapur's (entropy criterion) and Otsu's (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapur's method.