One of the areas of our interest at the moment is the evaluation of SSIM index family, as a set of useful tools to analyze several aspects of radiological images. These metrics are based on the analysis of the structural similarity of two images. They are: 

	Metrics based on the three components of SSIM: luminance, contrast and structure.
SSIM  (1)	The original Structural SIMilarity index.
G-SSIM (4)	Calculates SSIM over the gradient version of the image.
MS-SSIM (2)	Multiscale version of SSIM.
MS-G-SSIM (5)	Multiscale version of G-SSIM.
	4-component versions (weighting region type) of the four previous metrics
4-SSIM (5)	Weights the values of the SSIM map according to the change (or preservation) of the original image’s texture.
4-G-SSIM (5)	Equal to 4-SSIM, but the original images are replaced by their gradient versions.
4-MS-SSIM (5)	Multiscale version of 4-SSIM. 4-SSIM is calculated for every scale and then pooled according to the MS-SSIM rules.
4-MS-G-SSIM (5)	Multiscale version of 4-G-SSIM.
	Metrics based on the structural component of SSIM: r*
r* (6)	The structural component of SSIM index, as proposed by Rouse and Hemami.
G-r* (6)	Calculates r* over the gradient version of the image.
MS-r* (3)	Multiscale version of r*. Is equivalent to the R* index proposed by Rouse and Hemami.
MS-G-r* (6)	Multiscale version of G-r*.
	4-component versions (weighting region type) of the four previous metrics
4-r* (6)	Weights the values of the r* map according to the change (or preservation) of the original image’s texture.
4-G-r* (6)	Equal to 4-r*, but the original images are replaced by their gradient versions.
4-MS-r* (6)	Multiscale version of 4-r*. The 4-structural component is calculated for every scale and then pooled according to the MS-r* rules.
4-MS-G- r* (6)	Multiscale version of 4-G- r*.

 As an intermediate result, we have developed  in Java a plugin version for  ImageJ that calculates these indexes.  It is our aim to share  our  programs as Open Source Software with the scientific community.

 In this web you can download this software tool (developed as a ImageJ plugin). Also you can download some test images with our results. 

 

 (1) Wang Z, Bovik AC; Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on. 2004. (13). no.4. p. 600-612.

(2) Wang Z, Simoncelli E, and Bovik AC. Multi-scale structural similarity for image quality assessment. Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers; 2003. p. 529–554.

(3) Rouse DM and Hemami SS. Analyzing the Role of Visual Structure in the Recognition of Natural Image Content with Multi-Scale SSIM. Proceedings of SPIE. 2009. p. 6806.

(4) Chen GH, Yang CL, Xie SL, Gradient-based structural similarity for image quality assessment. IEEE International Conference on Image Processing; 2006. p. 2929–2932.

(5) Li C and Bovik AC. Content-partitioned structural similarity index for image quality assessment. Journal Image Communication; 2010. (25), no. 7, p. 517-526.

(6) Gabriel Prieto Renieblas, Agustín Turrero Nogués, Alberto Muñoz González, Nieves Gómez-Leon, Eduardo Guibelalde del Castillo, "Structural similarity index family for image quality assessment in radiological images," J. Med. Imag. 4(3), 035501 (2017), doi: 10.1117/1.JMI.4.3.035501.