Compression of 99mTc Methylene Diphosphonate Bone Scan Images using Discrete Cosine Transformation

Pandey, AnilKumar; Yadav, Priya; Chaudhary, Jagrati; Sharma, ParamDev; Jaleel, Jasim; Patel, Chetan; Bal, Chandrasekhar; Kumar, Rakesh

doi:10.4103/ijnm.ijnm_45_22

Cited by 1 publication

(1 citation statement)

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“…Thus, based on our previous experience, we choose the four different thresholds. [ 9 ] All experiments were performed on a 3.30GHz i3-2120 CPU with 2 GB RAM, running 64-bit Microsoft Windows operating system. A personal computer program was written in MATLAB R2020b (The Math Works, Inc. 3 Apple Hill Drive Natick, MA 01760-2098) to read and compress DICOM images using DCT.…”

Section: Methodsmentioning

confidence: 99%

Near-lossless Compression of Tc-99 m DMSA Scan Images Using Discrete Cosine Transformation

Yadav,

Pandey,

Chaudhary

et al. 2023

Indian Journal of Nuclear Medicine

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Aim and Objective: The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation. Materials and Methods: Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05. Results: At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively. Conclusions: Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.

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Section: Methodsmentioning

confidence: 99%