Artificial Immune System Based Image Enhancement Technique

Ganguli, Susmita; Mahapatra, Prasant Kumar; Kumar, Amod

doi:10.1007/978-3-319-11218-3_1

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Workflow Of Improved Clonal Selection Algorithm1

Introduction1

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2015

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(2 citation statements)

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“…4) Do the mutation operation on the antibody in the clone set C, according to the Gauss distribution, the uniform distribution and the chaotic distribution. We use the affinity function to evaluate the new individuals after the cloning operation and the mutation operation, and generate new antibody set C*.…”

Section: Workflow Of Improved Clonal Selection Algorithmmentioning

confidence: 99%

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Improved immune algorithm for medical image enhancement

Gong

Fan

Pei

et al. 2015

2015 International Workshop on Artificial Immune Systems (AIS)

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Traditional clonal selection algorithm gives us some inspiration for many applications, but in the medical image enhancement application we found some defects of this algorithm. So we improved the clonal selection algorithm in three ways. First, we designed the real coding of the MRI brain image, instead of binary coding. Second, we added the mutation distance to control the mutation progress and avoid only the local optimization. In addition, we adjust the clone selection and the mutation together in the Gauss distribution, the uniform distribution, and the chaotic distribution, rather than in only the Gauss distribution. Then we use the real MRI brain images to test the image enhancement of our improved clonal selection algorithm. The experimental results show that our approach outperform the median filtering (MF) and the adaptive template filtering (ATF) in enhancing the MRI brain images.

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Section: Workflow Of Improved Clonal Selection Algorithmmentioning

confidence: 99%

“…Inspired from the artificial immune systems and immune algorithms, the image enhancing was improved with the new intelligent systems and algorithms [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%