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    Journals >Optics and Precision Engineering >Volume 31 >Issue 13 >Page 1973 > Article
    • Optics and Precision Engineering
    • Vol. 31, Issue 13, 1973 (2023)
    Automatic segmentation of aggregate images with MET optimized by chaos SSA
    Mengfei WANG1, Weixing WANG1,*, and Limin LI2,*
    Author Affiliations
  • 1College of Information， Chang'an University， Xi'an70064， China
  • 2School of Electrical and Electronic Engineering， Wenzhou University， Wenzhou35035， China
    • show less
    DOI: 10.37188/OPE.20233113.1973 Cite this Article
    Mengfei WANG, Weixing WANG, Limin LI. Automatic segmentation of aggregate images with MET optimized by chaos SSA[J]. Optics and Precision Engineering, 2023, 31(13): 1973 Copy Citation Text show less
    Logistic mapping
    Fig. 1. Logistic mapping
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    Aggregate segmentation images of MET at K=3
    Fig. 2. Aggregate segmentation images of MET at K=3
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    Flowchart of LSSA-MET
    Fig. 3. Flowchart of LSSA-MET
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    Schematic diagrams of F1 and F4 at D=2
    Fig. 4. Schematic diagrams of F1 and F4 at D=2
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    Convergence curves of optimization algorithms
    Fig. 5. Convergence curves of optimization algorithms
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    Aggregate images
    Fig. 6. Aggregate images
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    Segmented images
    Fig. 7. Segmented images
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    Local segmentation results of aggregate image No.2 at K=6
    Fig. 8. Local segmentation results of aggregate image No.2 at K=6
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    方 法F1F2
    BestAVGSDT/sBestAVGSDT/s
    PSO8.75×10-113.47×10-96.08×10-969.9452.64×10-11.568.07×10-168.919
    GWO4.93×10-454.01×10-439.02×10-4364.1204.12×10-126.52×10-118.46×10-1160.733
    WOA1.07×10-1269.61×10-1116.10×10-11023.0232.07×10-14.79×102.44×1023.022
    MA9.48×10-121.43×10-103.42×10-10174.0362.81×104.24×105.54170.238
    SSA3.10×10-2582.22×10-501.71×10-4920.3161.03×10-531.96×10-101.25×10-920.210
    CASSA09.93×10-536.72×10-3919.1623.76×10-276.38×10-133.79×10-1120.661
    CDLSSA00041.61401.43×10-204040.259
    LSSA00021.36700020.497
    方 法F3F4
    BestAVGSDT/sBestAVGSDT/s
    PSO9.175.51×108.35×10+172.211-9.07×103-7.86×1036.17×10272.674
    GWO2.52×102.65×107.21×10-164.341-7.23×103-6.02×1035.78×10264.946
    WOA2.60×102.70×107.08×10-126.039-1.26×104-9.25×1031.46×10325.942
    MA8.06×10-14.68×105.19×10+1183.103-1.11×104-1.02×1043.19×102185.479
    SSA1.21×10-74.91×10-46.97×10-322.811-9.91×103-8.86×1031.73×10322.903
    CASSA1.19×10-77.40×10-42.01×10-322.548-9.29×103-9.76×1031.86×10322.432
    CDLSSA1.03×10-71.19×10-35.70×10-344.432-1.26×104-1.04×1041.03×10243.501
    LSSA1.16×10-84.45×10-41.91×10-323.025-1.26×104-1.08×1041.00×10223.011
    方 法F5F6
    BestAVGSDT/sBestAVGSDT/s
    PSO2.49×105.63×101.36×1070.8191.04×10-111.07×10-11.98×10-1100.284
    GWO07.56×10-12.0662.1351.68×10-62.53×10-21.52×10-294.983
    WOA00023.3416.67×10-41.66×10-21.13×10-250.255
    MA4.982.09×101.06×10176.5032.07×10-99.10×10-22.12×10-1256.946
    SSA00020.3502.39×10-101.21×10-62.46×10-648.751
    CASSA00020.5439.53×10-115.80×10-72.56×10-648.613
    CDLSSA00040.0182.58×10-115.10×10-71.65×10-688.256
    LSSA00020.1551.09×10-123.70×10-89.65×10-748.639
    Table 1. Evaluation parameters
    View in the Article
    图像参数Renyi熵对称交叉熵Kapur熵
    LSSASSALSSASSALSSASSA
    No.1阈值

    41 74 115

    150 186 227

    35 62 95

    131 171 228

    30 56 92

    121 154 185

    32 55 92

    113 149 172

    41 73 102

    133 161 192

    39 72 94

    124 169 191

    适应度值24.356 624.237 9241 731.634266 481.176 924.124 923.974
    No.2阈值

    44 76 108

    167 142 199

    45 71 92

    114 137 189

    32 65 101

    131 157 183

    36 73 109

    133 150 178

    37 75 106

    134 162 194

    40 61 80

    118 151 190

    适应度值24.305 923.971 1239 635.992 8262 853.106 624.091 323.926 6
    No.3阈值

    37 71 140

    14 173 208

    39 71 104

    137 169 201

    25 43 81

    109 143 173

    29 53 91

    126 139 166

    38 70 105

    136 168 199

    43 77 100

    124 150 185

    适应度值24.416 424.409 5263 073.381 9284 278.489 624.142 123.989
    No.4阈值

    36 69 100

    131 163 193

    53 80 101

    122 149 176

    35 65 95

    126 157 183

    37 63 95

    126 154 183

    41 72 101

    129 159 190

    43 85 109

    134 160 183

    适应度值24.088 923.795 7183 027.266 6183 338.434 223.824 823.693 1
    Table 2. 时LSSA-MET与SSA-MET的阈值和适应度值
    View in the Article
    图像KRenyi EntropySymmetric Cross EntropyKapur EntropyFCM
    LSSASSALSSASSALSSASSA
    No.1213.013.112.012.012.412.412.8
    417.917.215.815.617.316.412.3
    624.422.717.315.419.219.48.46
    No 2213.313.212.412.413.113.111.3
    417.617.015.814.216.916.510.3
    620.415.717.416.419.216.87.51
    No.3213.513.511.511.512.612.612.4
    418.317.614.113.717.616.511.0
    622.921.614.714.721.317.06.41
    No.4213.013.013.613.512.612.612.0
    418.217.917.417.417.317.110.7
    621.516.819.018.720.219.07.69
    优值个数917071—
    Table 3. PSNR values
    View in the Article
    图像KRenyi EntropySymmetric Cross EntropyKapur EntropyFCM
    LSSASSALSSASSALSSASSA
    No.123.29×10-13.31×10-13.15×10-13.15×10-13.20×10-13.20×10-13.66×10-1
    45.19×10-15.11×10-15.14×10-15.09×10-15.20×10-15.12×10-12.39×10-1
    66.21×10-15.95×10-16.35×10-15.97×10-16.32×10-16.11×10-12.68×10-2
    No 223.72×10-13.70×10-13.61×10-13.62×10-13.68×10-13.68×10-11.24×10-1
    45.61×10-15.56×10-15.56×10-15.30×10-15.59×10-15.56×10-13.81×10-1
    66.67×10-16.03×10-16.63×10-16.27×10-16.69×10-16.36×10-12.28×10-2
    No.323.52×10-13.52×10-13.31×10-13.31×10-13.48×10-13.48×10-16.17×10-1
    45.48×10-15.49×10-15.40×10-15.32×10-15.50×10-15.38×10-12.29×10-2
    66.78×10-16.74×10-16.47×10-16.21×10-16.74×10-16.46×10-13.47×10-3
    No.423.06×10-13.06×10-13.21×10-13.18×10-12.96×10-12.96×10-12.84×10-1
    45.18×10-15.09×10-15.10×10-15.02×10-15.09×10-15.04×10-11.46×10-1
    66.29×10-16.02×10-16.26×10-16.18×10-16.29×10-16.14×10-11.11×10-2
    优值个数829180—
    Table 4. SSIM values
    View in the Article
    图像KRenyi EntropySymmetric Cross EntropyKapur EntropyFCM
    LSSASSALSSASSALSSASSA
    No.127.40×10-17.41×10-17.32×10-17.32×10-17.32×10-17.32×10-17.67×10-1
    48.81×10-18.77×10-18.76×10-18.66×10-18.81×10-18.69×10-17.08×10-1
    69.33×10-19.09×10-19.27×10-18.96×10-19.24×10-19.12×10-16.40×10-1
    No 227.59×10-17.57×10-17.49×10-17.50×10-17.52×10-17.53×10-17.66×10-1
    48.92×10-18.84×10-18.83×10-18.61×10-18.87×10-18.80×10-17.64×10-1
    69.32×10-18.87×10-19.19×10-18.89×10-19.33×10-19.17×10-16.90×10-1
    No.327.55×10-17.55×10-17.29×10-17.29×10-17.44×10-17.44×10-17.05×10-1
    48.91×10-18.84×10-18.77×10-18.69×10-18.90×10-18.76×10-16.41×10-1
    69.46×10-19.22×10-19.16×10-18.92×10-19.41×10-19.11×10-16.05×10-1
    No.427.21×10-17.21×10-17.34×10-17.33×10-17.12×10-17.11×10-16.93×10-1
    48.71×10-18.64×10-18.66×10-18.61×10-18.65×10-18.58×10-16.80×10-1
    69.28×10-18.87×10-19.21×10-19.11×10-19.25×10-19.02×10-16.72×10-1
    优值个数919191—
    Table 5. FSIM values
    View in the Article
    KRenyi EntropySymmetric Cross EntropyKapur EntropyFCM
    LSSASSALSSASSALSSASSA
    21.421 s1.422 s1.498 s1.498 s1.440 s1.440 s1.667 s
    41.461 s1.453 s1.546 s1.564 s1.496 s1.476 s4.386 s
    61.472 s1.491 s1.618 s1.603 s1.507 s1.489 s8.602 s
    优值个数211102—
    Table 6. T values
    View in the Article
    算 法PSNRSSIMFSIMT/s
    LSSA-Renyi熵1.78×105.08×10-18.54×10-11.451
    LSSA-对称交叉熵1.51×105.02×10-18.44×10-11.554
    LSSA-Kapur熵1.66×105.06×10-18.49×10-11.481
    Table 7. Parameter statistical mean of LSSA-MET
    View in the Article
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    Mengfei WANG, Weixing WANG, Limin LI. Automatic segmentation of aggregate images with MET optimized by chaos SSA[J]. Optics and Precision Engineering, 2023, 31(13): 1973
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