CAJ | 학술논문

为了实现甲状腺肿瘤B超图像和核素图像的图像配准融合以及得到特征级图像配准所需要的特征区域，需分割出甲状腺、肿瘤及其周围组织。这类图像在形成过程中往往会产生斑点噪声使图像质量较差，且具有灰度对比度低和亮度分布不均匀等特点，提出一种基于各向异性扩散的归一化割分割法，将各向异性扩散模型引入到归一化割中，并通过调节模型参数来对甲状腺肿瘤B超图像进行去噪和边缘增强，优化了归一化割中轮廓线图和权值矩阵，在一定程度上避免了归一化割的过分割和欠分割，实验结果证明了该方法的可行性。
위료실현갑상선종류B초도상화핵소도상적도상배준융합이급득도특정급도상배준소수요적특정구역，수분할출갑상선、종류급기주위조직。저류도상재형성과정중왕왕회산생반점조성사도상질량교차，차구유회도대비도저화량도분포불균균등특점，제출일충기우각향이성확산적귀일화할분할법，장각향이성확산모형인입도귀일화할중，병통과조절모형삼수래대갑상선종류B초도상진행거조화변연증강，우화료귀일화할중륜곽선도화권치구진，재일정정도상피면료귀일화할적과분할화흠분할，실험결과증명료해방법적가행성。
In order to realize image registration and fusion of thyroid tumor B-type ultrasound image and radionuclide image and get features areas which registration of the characteristic class required, it is necessary to segment thyroid, the thyroid tumor and surrounding tissue. Some spot noises make the image poor quality in the process of formate ultrasonic images and the image is characterized with low contrast and intensity inhomogeneity. An improved normalized cut segmentation method is proposed. Anisotropic diffusion is introduced into the normalized cut, avoiding the problem of over-and incomplete segmentation, denois-ing and enhancing edge for thyroid tumor ultrasonic image by adjusting the model parameters, optimizing edge figure and weight matrix. The experimental results confirm the feasibility.