Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea.
Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan 76127, Indonesia.
Biosensors (Basel). 2021 Dec 8;11(12):504. doi: 10.3390/bios11120504.
Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties' distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.
漫射光学断层成像是一种新兴的非侵入性光学方法，通过获取光学特性的分布来评估组织信息。该方法通过两种程序来生成重建的吸收和散射图像，这些图像提供的结构信息可在边界周围已知光强的辅助下用于定位组织内的包含物。这些方法被称为正向问题和逆解。一旦获得重建图像，就会使用主观测量作为评估图像的传统方法。因此，在这项研究中，我们开发了一种算法，旨在使用结构相似性（SSIM）指数对重建图像进行数值评估，以识别包含物。我们比较了四种 SSIM 算法与 168 张模拟重建图像，这些图像的包含物位置相同，但对比度和包含物大小不同。提出了一种包含锐度参数的多尺度改进 SSIM（MS-ISSIM-S）算法，以代表与人类可见感知相比的潜在评估。结果表明，所提出的 MS-ISSIM-S 适合人类视觉感知，因为它在具有相似包含物大小的情况下降低了与各种对比度相关的相似性得分；因此，该指标有望用于客观地评估漫射光学重建图像。
