Division of Laser and Atomic Research and Development, Ruder Bosković Institute, 10002 Zagreb, Croatia.
J Photochem Photobiol B. 2010 Jul 2;100(1):10-8. doi: 10.1016/j.jphotobiol.2010.03.013. Epub 2010 Apr 3.
This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude.
本研究旨在展示基于新的依赖成分分析 (DCA) 的方法在基底细胞癌 (BCC) 分割方面的强大性能，该方法通过对 BCC 的红绿蓝 (RGB) 荧光图像进行无监督分解来实现。DCA 算法的尺度不变性特性使其具有对强度波动的鲁棒性，该特性利用了 BCC 与周围组织之间的光谱和空间多样性。基于滤波的 DCA 方法是独立成分分析 (ICA) 的扩展，对于解释 BCC 和周围组织之间的光谱相似性所引起的统计依赖性是必要的。这会产生较弱的边缘，这对其他分割方法也是一个挑战。通过与最先进的图像分割方法（如活动轮廓（水平集）、K-均值聚类、非负矩阵分解、ICA 和比率成像）的性能对比分析，我们在两个要求苛刻的场景中实验证明了 DCA 方法在 BCC 分割方面的良好性能，其中荧光图像的强度变化了几乎两个数量级。
