An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation

•The ALDE algorithm works better for MRI image analysis than DE variants.•The ALDE algorithm is less sensitive to increasing number of thresholds.•The ALDE algorithm is fast enough for real-world image analysis applications.•The ALDE algorithm can efficiently balance between exploration and exploita...

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Vydané v:Expert systems with applications Ročník 138; s. 112820
Hlavní autori: Tarkhaneh, Omid, Shen, Haifeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 30.12.2019
Elsevier BV
Predmet:
Adaptive algorithms
Algorithms
Benchmarks
Brain
Cauchy distribution
Cotes’ Spiral
Diagnostic systems
Differential evolution
Efficiency
Evolutionary algorithms
Evolutionary computation
Exploitation
Exploration
Image analysis
Image segmentation
Lévy distribution
Magnetic resonance imaging
Medical imaging
Mutation
Optimal thresholding
Thresholds
Differential evolution
Image segmentation
Lévy distribution
Optimal thresholding
Cauchy distribution
Cotes’ Spiral
ISSN:0957-4174, 1873-6793
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Shrnutí:•The ALDE algorithm works better for MRI image analysis than DE variants.•The ALDE algorithm is less sensitive to increasing number of thresholds.•The ALDE algorithm is fast enough for real-world image analysis applications.•The ALDE algorithm can efficiently balance between exploration and exploitation. Segmentation is an important method for MRI medical image analysis as it can provide the radiologists with noninvasive information about a patient that is crucial to the diagnostic process. The efficiency of such a computer-aided diagnosis system relies on the accuracy of an adopted image segmentation method. Multi-level thresholding is a segmentation method that has been widely adopted in medical image analysis in recent studies, where selecting the optimal thresholds has a pivotal role in determining the efficiency and the accuracy of the segmentation algorithm. While some well-known methods, such as Kapur’s and Otsu’s, are proven effective for bi-level thresholding, multi-level thresholding remains a challenge as it is computationally expensive. Evolutionary algorithms, such as Differential Evolution (DE), have the potential to address this problem, as they can find sufficiently good solutions with manageable computational effort. While a number of DE solutions have been proposed for multi-level thresholding, they are not stable, in that, when the number of thresholds increases, the algorithm efficiency decreases due to the imbalance between exploration and exploitation. In this paper, we propose a DE solution that achieves a good balance between exploration and exploitation through a new adaptive approach and new mutation strategies. The new adaptive approach can generate optimal solutions in assigning populations by measuring the quality of candidate solutions to evaluate the efficiency of different parts of the proposed DE algorithm. The new mutation methods harness Mantegna Lévy and Cauchy distributions, as well as Cotes’ Spiral to improve global search, and to further balance between exploitation and exploration. We further experimentally compare the proposed DE algorithm, referred to as Adaptive Differential Evolution with Lévy Distribution (ALDE), against three DE benchmark algorithms on T2 weighted MRI brain images. Our results show that ALDE can, not only obtain optimal thresholds at a reasonable computational cost, but more importantly, clearly outperforms the benchmark algorithms.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.07.037