Simultaneous reconstruction, segmentation, and edge enhancement of relatively piecewise continuous images with intensity-level information.
Simultaneous reconstruction, segmentation, and edge enhancement of relatively piecewise continuous images with intensity-level information.
复制标题
具有强度级信息的相对分段连续图像的同时重建、分割和边缘增强。
DOI：
10.1118/1.596685
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发表时间：
1991
期刊：
影响因子：
3.8
通讯作者：
Johnson,V
中科院分区：
文献类型：
--
作者：
Liang,Z;Jaszczak,R;Coleman,R;Johnson,V
提出了一种利用强度级信息重建连续图像区域的多项图像模型。强度水平信息假设图像强度在图像像素阵列上的连续区域内相对恒定，并且这些区域的强度水平由经验或理论上的信息标准确定。这些条件可能是有效的，例如，对于心脏血池成像，心肌、血池和背景区域的强度水平（或放射性核素活性）是不同的，而每个肌肉、血液或背景区域内的活动相对均匀。为了测试该模型，在64×64阵列上构建了一个数学模型。这个幻影有三个连续的区域。每个区域有不同的强度水平。利用发射层析成像几何技术模拟了来自幻体的测量结果。在180°上产生了50个投影，每个投影有64条等间距的平行光线。投影数据随机化以包含泊松噪声。图像重建使用迭代的最大后验概率程序进行。自动分割出三个强度等级对应的连续区域。同时，对区域边缘进行锐化处理。重构图像中的噪声被明显抑制。观察到迭代过程对幻影的收敛性。与最大似然法和过滤反投影法相比，使用最大似然概率法和强度水平信息获得的结果表明，在定位不同强度区域方面，定性和定量都有改善。
A multinomial image model is proposed which uses intensity‐level information for reconstruction of contiguous image regions. The intensity‐level information assumes that image intensities are relatively constant within contiguous regions over the image‐pixel array and that intensity levels of these regions are determined either empirically or theoretically by information criteria. These conditions may be valid, for example, for cardiac blood‐pool imaging, where the intensity levels (or radionuclide activities) of myocardium, blood‐pool, and background regions are distinct and the activities within each region of muscle, blood, or background are relatively uniform. To test the model, a mathematical phantom over a 64×64 array was constructed. The phantom had three contiguous regions. Each region had a different intensity level. Measurements from the phantom were simulated using an emission‐tomography geometry. Fifty projections were generated over 180°, with 64 equally spaced parallel rays per projection. Projection data were randomized to contain Poisson noise. Image reconstructions were performed using an iterative maximumaposterioriprobability procedure. The contiguous regions corresponding to the three intensity levels were automatically segmented. Simultaneously, the edges of the regions were sharpened. Noise in the reconstructed images was significantly suppressed. Convergence of the iterative procedure to the phantom was observed. Compared with maximum likelihood and filtered‐backprojection approaches, the results obtained using the maximumaposterioriprobability with the intensity‐level information demonstrated qualitative and quantitative improvement in localizing the regions of varying intensities.