# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Magnetic Resonance Imaging  
To the best of our knowledge, the first SSIM implementation for evaluating the images of MR was in 2005 [82; 83]. The attempt in [83] used distorted MR images and then compared them with the original MR image. Three modules creating a closed-loop system containing de-noising filters, evaluations method, and adjustment rules were used. The corrupted images were examined using SSIM along with the mean absolute error (MAE), root-mean-square error (RMSE), SNR, and PSNR. Their results indicate that SSIM shows a high similarity score when the distorted images are close to the reference image. This is the first time an exploration of the original SSIM was started for use in other applications, such as MR medical images. As we mentioned previously, the original SSIM may interpret the blurred images with a high similarity score owing to the effect of the structural comparison. However, their results indicate that SSIM has the potential to assess medical images. In addition, in [82], the SSIM showed a relatively decent performance to quantify the head MR images. However, there were inconsistencies in the 40% and 70% quality factors. Various quality factors were deployed to acquire the similarity value. Nonetheless, with the higher  
Figure 14: Demonstration using DDSM right breast masses benign cancer with CC view to present (**a**) preserved edge pixel image, (**b**) changed edge pixel image, (**c**) smooth pixel image, and (**d**) texture pixel image. Figure 15: Demonstration using DDSM right breast masses benign cancer with CC view to present the comparison among (**a**) original, (**b**) vertical translation, and (**c**) JPEG compressed images with a green line indicating the image vertical center. quality factor of compression, the SSIM predicted the similarity with a low score and was deemed unsuitable for the quality metric in [82]. In 2007, a group of researchers applied an SSIM application for security in the MR and computed tomography (CT) images related to the watermarked medical images [31]. Medical image watermarks are crucial because they may comprise the medical information of the patients, proving ownership, and alternating location on the images. Moreover, watermarked medical images frequently store hidden messages for later extraction to obtain the reports. Their results showed that SSIM is less capable of measuring the degradation of medical images when the images are embedded with a watermark. They speculated that the best metric for a watermarking measure is a steerable visual difference predictor (SVDP). By contrast, SSIM and a quality index based on local variance (QILV) have been exploited for the quality measure of estimating the magnitude of MR based on the linear minimum mean squared error (LMMSE) [84]. The SSIM in this task performed competitively in measuring images fused with an LMMSE estimator. The compression of medical images is challenging in teleraldiology because teleraldiology requires transmission to transfer the images [85]. In the transferring process, the image quality may be reduced owing to the limited communication, and thus the image fidelity can be decreased. The quality measure is necessary to determine the threshold value to compress these medical images; hence, in the transmission, the important information of the images is not diminished. A partitioning in hierarchical tree (SPIHT) compression algorithm has also been used to determine the maximum threshold standard for compression. The compressed images were compared with their original to assess their similarity. In this specific task, SSIM and PSNR showed agreeable results with the mean opinion score (MOS), and thus for this case, SSIM is considered appropriate to cut off the threshold value when the medical images are compressed with a defined bit rate. Subsequently, medical image fusion to improve the confidence of radiologists in diagnosing a specific disease was accomplished in 2009 by [PERSON] and [PERSON] [86].