Denoising of iodine-131 images using a median filter

Designing and Comparing Performances of Image Processing Pipeline for Enhancement of I-131-metaiodobenzylguanidine Images

Objective

An image processing pipeline can have more than one image processing technique in sequence, and the output of the first technique becomes input for the next technique and so on. In this study, we have designed and compared the performances of image processing pipelines for enhancement of I-131-metaiodobenzylguanidine (mIBG) images.

Materials and Methods

Five different image processing pipelines (A [Gaussian filter, normalization], B [histogram specification (image 1), Gaussian filter, normalization], C [histogram specification (image 2), Gaussian filter, normalization], D [{histogram specification (image 3), Gaussian filter, and normalization], and E [histogram specification (image 4), Gaussian filter, normalization]) were designed and their performances were evaluated on I-131-mIBG images (n = 122). The image quality was assessed objectively using Perception-based Image Quality Evaluator (PIQE) score and subjectively (on scale 1-4) by two nuclear medicine physician. Sign test was applied to find the statistically significant difference between the image quality obtained using image processing pipelines. We applied test of proportion to compute difference in proportion of image quality score assigned to images obtained using image processing pipelines.

Results

Based on PIQE score, the quality of images obtained using all the five image processing pipelines were significantly better than that of input images (P < 0.001). The highest image quality score (=4) was assigned maximum number of times (n = 90) to the images obtained using image processing pipeline D and was significantly different from that of the second best image processing pipeline E (P = 0.015).

Conclusions

The image processing pipeline D was found to be better for enhancement of I-131-mIBG images.

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

The objective of this study was to compare the performance of variance, median absolute deviation, and the square of median absolute deviation methods of noise estimation in denoising of ^99mTc-sestamibi parathyroid images using wavelet transform. Sixty-eight ^99mTc-sestamibi parathyroid images including 33 images acquired at zoom 1.0 and 35 acquired at zoom 2.0 were denoised using the wavethresh package in R. The image decomposition and reconstruction method discrete wavelet transform, wavelet filter db4, shrinkage method hard, and thresholding policy universal were used. The noise estimation in the process was made using var, mad and madmad functions, which use variance, mean absolute deviation, and the square of mean absolute deviation, respectively. The quality of denoised images was assessed both qualitatively and quantitatively. A nonparametric two-sample Kolmogorov–Smirnov test was applied to find whether the difference in image quality produced by these three noise estimation methods was significant at 95% confidence. Noise estimation using madmad function produced the best quality denoised image. Further, the quality of the denoised image using madmad function was significantly better than the quality of the denoised image obtained with var or mad function (P = 1). The estimation of noise using madmad functions in wavelet transforms provides the best-denoised image for both zoom 1.0 and zoom 2.0 ^99mTc-sestamibi parathyroid images.