Bhateja Vikrant, Moin Aisha, Srivastava Anuja, Bao Le Nguyen, Lay-Ekuakille Aimé, Le Dac-Nhuong
Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh 226028, India.
Duytan University, Danang 550000, Vietnam.
Rev Sci Instrum. 2016 Jul;87(7):074303. doi: 10.1063/1.4959559.
Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).
基于计算机的阿尔茨海默病诊断可以通过分析大脑的功能和结构变化来实现。多光谱图像融合考虑互补信息的融合，同时丢弃多余信息，以获得一个包含空间和光谱细节的单一图像。本文提出了一种基于非下采样轮廓波变换（NSCT）的多光谱图像融合模型，用于阿尔茨海默病的计算机辅助诊断。所提出的融合方法包括对输入多光谱图像进行颜色变换。在YIQ颜色空间中的多光谱图像使用NSCT进行分解，然后对低频系数使用改进的主成分分析算法进行降维。此外，使用非线性增强函数增强高频系数。然后将两种不同的融合规则应用于低通和高通子带：将相位一致性应用于低频系数，将方向对比度和归一化香农熵的组合应用于高频系数。通过与其他现有融合方法（在各种融合指标方面）进行比较，展示了融合响应的优越性。
