CAJ | 학술논문

针对甲状腺肿瘤超声图像复杂度高和SPECT图像边界模糊的特点，结合Shearlet变换能够捕捉图像细节信息和果蝇优化算法可靠性高的优势，提出了Shearlet变换和果蝇优化算法相结合的图像融合算法。首先，用Shearlet变换对已精确配准的源图像进行分解，分别得到高低频子带系数。高频子带系数采用区域能量取大的融合规则，低频子带系数使用改进的加权融合规则，并把果蝇优化算法引入低频融合过程，以互信息作为适应度函数来获取最优值，克服了原加权融合算法互信息低的缺点。最后，用Shearlet逆变换得到融合后的图像。实验结果表明，此算法在主观视觉效果和客观评价指标上优于其他融合算法。
침대갑상선종류초성도상복잡도고화SPECT도상변계모호적특점，결합Shearlet변환능구포착도상세절신식화과승우화산법가고성고적우세，제출료Shearlet변환화과승우화산법상결합적도상융합산법。수선，용Shearlet변환대이정학배준적원도상진행분해，분별득도고저빈자대계수。고빈자대계수채용구역능량취대적융합규칙，저빈자대계수사용개진적가권융합규칙，병파과승우화산법인입저빈융합과정，이호신식작위괄응도함수래획취최우치，극복료원가권융합산법호신식저적결점。최후，용Shearlet역변환득도융합후적도상。실험결과표명，차산법재주관시각효과화객관평개지표상우우기타융합산법。
According to the characteristics of ultrasound images with high complexity and SPECT image with blurred boundary, combining the advantage of the Shearlet transform can capture the detail information of images and the high reliability of the Fruit Fly Optimization Algorithm, an image fusion algorithm based on Shearlet trans-form and Fruit Fly Optimization Algorithm is proposed. Firstly, the Shearlet transform is used to decompose the reg-istered source images, thus the low frequency sub-band coefficients and high frequency sub-band coefficients can be obtained. The high frequency sub-band coefficients are fused by the region energy maximum. The fusion rule of the low frequency sub-band coefficients is based on the method of modified weighted fusion, in order to overcome the disadvantage of low mutual information in primary weighted fusion algorithm, the Fruit Fly Optimization Algorithm is introduced in fusion process, the mutual information as fitness function is used to calculate the optimum solution. Finally, the fused image is reconstructed by inverse Shearlet transform. The experimental results demonstrate that the proposed method outperforms the other methods in term of visual evaluation and objective evaluation.