# Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Mathematical Framework for Extraction of the ROI and NROI

#### 2.2. Features Derived from the $GLCM$

#### 2.3. Classification of the Image Compression Algorithm

#### 2.3.1. Transform Coding

#### 2.3.2. Fractal Compression

- The achievable compression ratios range from 1:4 to 1:100, making it efficient in reducing data size.
- The compression phase is notably slower compared with other methodologies.
- Decoding, on the other hand, is expedited and is not bound by resolution constraints.
- Natural images, with their inherent similarities and patterns, are best suited for this compression method.
- The algorithm exhibits superior performance with color images as opposed to grayscale ones.

#### 2.3.3. Chroma Sampling

#### 2.3.4. Discrete Cosines Transform

#### 2.3.5. Vector Quantization Algorithm

**Figure 4.**Encoding–decoding procedure corresponding to the VQA [70].

#### 2.3.6. Run-Length Encoding

#### 2.3.7. Entropy Encoding

#### 2.3.8. Lempel–Ziv–Welch Image Compression

#### 2.3.9. DEFLATE Image Compression

#### 2.4. Performance Measures for Image Compression

#### 2.4.1. Mean Square Error

#### 2.4.2. The Peak Signal to Noise Ratio

#### 2.4.3. Compression Ratio

#### 2.4.4. Bits per Pixels

## 3. Results

#### 3.1. ROI and NROI-Based Compression of the DICOM Image

#### 3.2. Texture Quantification of the Time-Series CT Chest Images

_{1}and I

_{2}lies mainly on the negative side and is below zero. The contrast of the average texture features between I

_{2}and I

_{3}is slightly above zero and lies on the positive side. Finally, the difference between the average texture features of I

_{3}and I

_{4}lies mainly toward the opposing side and is below zero. Thus, several interpretations can be concluded from this study; for example, the maximum improvement in the human chest is developed between images I

_{2}and I

_{3}because the difference in the average texture is primarily positive. Thus, it can be concluded that using the GLCM-based texture analysis approach, the maximum amount of change developed in the CT chest scans of the human chest can be identified. The main advantage of this approach is that it provides “change information” based on the statistical arrangement of the image pixels. It is a practical methodology for change estimation. Here, GLCM application is presented for medical image processing. GLCM features are also considered unique because they can provide information on “spectral” and “spatial” arrangements. One of the shortcomings of this methodology is that it is too lengthy to implement as it contains several intermediate steps.

## 4. Discussion

_{2}and I

_{3}, there was a marked positive alteration in texture features, possibly indicative of a patient’s pronounced recovery during that phase. This study further established that image processing techniques are highly efficient for data compression and storage space reduction. Each image compression technique has its advantages and disadvantages. Therefore, in our work, we endeavored to integrate various compression methods to determine the most reliable technique for image compression. A significant number of procedures were used, where the ROI was compressed using certain techniques, while the NROI was compressed utilizing others. Ultimately, we identified the most ideal combination of techniques for the analysis of DICOM images. In the current era, with the evolution of machine learning and computational intelligence techniques, several new methodologies have been introduced in image processing that can certainly produce more sound, accurate, and better results than the algorithms used in this article. Some of these notable techniques include Neural network-based image compression [87], which uses deep learning-based architectures like Convolutional Neural Networks (CNNs). These algorithms are designed to read the data for efficient image compression. Wavelet transform [88] is not a machine learning-based technique. This technique is merged with a machine learning-based algorithm for effective image compression. Evolutionary algorithms [89,90] are quite popular in image compression. One of the very popular evolutionary algorithms includes the Genetic Algorithm (GA), which can optimize image parameters for better compression, efficiency, and performance. Another popular image compression algorithm includes Fuzzy-logic systems [91], which are designed to compress an image based on its content, characteristics, and features. Generative Adversarial Networks (GANs) [92] are used to compress the image content based on high fidelity. This algorithm can create compressed images that look the same as the original image. These images cannot be distinguished from their previous vision, but the property of the compressed image is completely different from the original image. Support vector machine (SVM) [93] techniques act as a middleman, whose purpose is feature extraction and the selection of image data, based on which a suitable compression technique is selected. In the Reinforcement Learning technique [94], compression strategies are decided based on specific image characters like the compression rate and image quality. Deep reinforcement learning-based techniques [95] are considered one of the most advanced image compression techniques in which compression strategies are decided based on the type of outcome. For example, if more compression is required, then algorithms are designed in that manner, and if low compression is required, then the compression approach changes automatically. Finally, using the Convolutional Autoencoder [96] algorithm, image data can be effectively compressed by learning the spatial hierarchies of features. Currently, Artificial Intelligence (AI)-based [97,98] algorithms are in trending. These algorithms have the potential to be designed for a specific purpose and could be used for image compression purposes. In summary, this research unequivocally underscores the potency of the dual approach of image compression and texture quantification in medical image analytics. In addition to its analytical prowess, the proposed image compression algorithm could be a game-changer in data storage efficiency—a boon for the medical fraternity, especially considering the anticipated third wave of COVID-19, where rapid, efficient data processing and storage will be paramount.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Block diagram of transform coding [39].

**Figure 2.**Block diagram of fractal geometry and transform coding (

**a**) Circular geometry (

**b**) Sierpinski triangle (

**c**) Block based compression (

**d**) Image compression methodology [40].

**Figure 3.**Block diagram of chroma subsampling [64].

**Figure 5.**Encoding model using run-length coding [74] IDS = input data stream, RLE = run-length-encoded.

**Figure 6.**Block diagram of entropy encoding [76].

**Figure 9.**The DEFLATE image compression algorithm: (

**a**) Huffman coding and (

**b**) a symbol frequency table.

**Figure 10.**$\mathrm{D}\mathrm{I}\mathrm{C}\mathrm{O}\mathrm{M}$ representation of the human lung (MicroDICOM) [85].

**Figure 14.**Zero-padded ROI compression using wavelet transform: (

**a**) compression factor of $0.5,$ (

**b**) compression factor of 0.1, (

**c**) compression factor of $0.025,$ and (

**d**) compression factor of $0.010$.

**Figure 15.**NROI compression using wavelet transform: (

**a**) compression factor of $0.5,$ (

**b**) compression factor of $0.1,$(

**c**) compression factor of $0.025,$ and (

**d**) compression factor of $0.010$.

**Figure 16.**Final fused image with a non-compressed ROI and a compressedNROI: (

**a**) compression factor of $\left(0.0,0.5\right),$ (

**b**) compression factor of $\left(0.5,0.5\right),$ (

**c**) compression factor of $\left(0.1,0.025\right),$ and (

**d**) compression factor of $(0.01,0.01)$.

**Figure 17.**Comparison of the perpetual image quality parameters: (

**a**) MSE, (

**b**) PSNR, (

**c**) BPP, (

**d**) CR, (

**e**) CT*, and (

**f**) DCT*.

**Figure 18.**Time series of CT scans of a human chest infected from COVID-19 virus (

**a**) Day 1 (

**b**) Day 7 (

**c**) Day 14 (

**d**) Day 21 [86].

**Figure 19.**Example of the GLCM calculation. (

**a**) Pixel position in the test image of dimensions $5\times 5.$ (

**b**) Pixels in the test image. (

**c**) GLCM obtained from the test image. (

**d**) Normalized GLCM of the test image.

**Figure 21.**Grey-level representation of the human chest infected with the COVID-19 virus in descending order of severity: (

**a**) Day 1, (

**b**) Day 7, (

**c**) Day 14, and (

**d**) Day 21.

**Figure 22.**Histogram signatures corresponding to time-series images of the human chest: (

**a**) Day 1, (

**b**) Day 7, (

**c**) Day 14, and (

**d**) Day 21.

**Figure 23.**Variation in the GLCM features of the human chest infected with COVID-19 ($\mathrm{D}\mathrm{a}\mathrm{y}1$): (

**a**) 0 degrees, (

**b**) 45 degrees, (

**c**) 90 degrees, (

**d**) and 135 degrees (1 = contrast, 2 = correlation, 3 = ASM, 4 = IDM).

**Figure 24.**Variation in the GLCM features of the human chest infected with COVID-19 $\left(\mathrm{D}\mathrm{a}\mathrm{y}7\right):$ (

**a**) 0 degrees, (

**b**) 45 degrees, (

**c**) 90 degrees, and (

**d**) 135 degrees (1 = contrast, 2 = correlation, 3 = ASM, 4 = IDM).

**Figure 25.**Variation in the GLCM features of the human chest infected with COVID-19 ($\mathrm{D}\mathrm{a}\mathrm{y}14$): (

**a**) 0 degrees, (

**b**) 45 degrees, (

**c**) 90 degrees, and (

**d**) 135 degrees (1 = contrast, 2 = correlation, 3 = ASM, 4 = IDM).

**Figure 26.**Variation in the GLCM features of the human chest infected with COVID-19 ($\mathrm{D}\mathrm{a}\mathrm{y}21$): (

**a**) 0 degrees, (

**b**) 45 degrees, (

**c**) 90 degrees, and (

**d**) 135 degrees (1 = contrast, 2 = correlation, 3 = ASM, 4 = IDM).

Chroma Subsampling | Features | Ref. |
---|---|---|

$4:4:4$ | In this sampling methodology, all three $YCbCr$ components have the same sampling rate as the input resolution. | [65,66] |

$4:2:2$ | In this sampling methodology, chroma subcomponents are sampled by a factor of 2, and their influential position is co-sited. | [65,66] |

$4:1:1$ | In this sampling methodology, $Cb$ and $Cr$ components are sampled by a factor of 4 horizontally and co-sited with the fourth brightness sample. | [65,66] |

$4:2:0$ | In this sampling methodology, $Cb$ and $Cr$ components are subsampled by a factor of 2 in both the horizontal and vertical directions. | [65,66] |

$4:1:0$ | This sampling methodology uses half of the vertical and one-fourth of the horizontal color resolution along with one-eighthof the bandwidth of the maximum color resolution. | [65,66] |

$3:1:1$ | In this sampling methodology, $Cb$ and $Cr$ components are subsampled by a factor of 3 horizontally. The chroma sample is later divided by every third brightness sample. The 36-byte $RGB$ elements are also reduced by 20, producing $2:1$ compression. | [66,67] |

S. No | Series | Scan Mode | mAs | KV | N*T | CTDIvo (mGy) | DLP (mGy*cm) | Phantom Type (cm) |
---|---|---|---|---|---|---|---|---|

1 | Mediastin | Surview | ----- | 120 | 2 × 0.75 | 0.05 | 1.93 | BODY32 |

2 | Mediastin | Helical | 80 | 120 | 16 × 1.50 | 5.55 | 170.49 | BODY32 |

S. No | NROI | Image Compression Algorithm | MSE | PSNR | BPP | CR | CT* (s) | DCT* (s) |
---|---|---|---|---|---|---|---|---|

1 | Figure 16a | Discrete cosine transform (DCT) | 0.0018 | 123.77 | 0.574 | 27.87 | 1.33 | 22.1 |

2 | Figure 16b | 0.0054 | 119.00 | 0.512 | 31.25 | 1.38 | 24.9 | |

3 | Figure 16c | 0.0652 | 108.18 | 0.482 | 33.19 | 1.41 | 26.4 | |

4 | Figure 16d | 0.3847 | 100.47 | 0.464 | 34.48 | 1.49 | 28.2 | |

5 | Figure 16a | Discrete wavelet transform (DWT) | 0.0008 | 127.29 | 0.422 | 37.91 | 1.51 | 23.1 |

6 | Figure 16b | 0.0032 | 121.29 | 0.389 | 41.13 | 1.58 | 25.6 | |

7 | Figure 16c | 0.0315 | 111.35 | 0.302 | 52.98 | 1.62 | 27.8 | |

8 | Figure 16d | 0.2752 | 101.93 | 0.232 | 68.96 | 1.68 | 28.4 | |

9 | Figure 16a | Fractal compression algorithm (FCA) | 0.0009 | 126.78 | 0.481 | 33.26 | 1.34 | 25.9 |

10 | Figure 16b | 0.0084 | 117.08 | 0.432 | 37.03 | 1.38 | 26.8 | |

11 | Figure 16c | 0.0542 | 108.98 | 0.354 | 45.19 | 1.45 | 27.7 | |

12 | Figure 16d | 0.4842 | 99.47 | 0.264 | 60.60 | 1.51 | 28.9 | |

13 | Figure 16a | Vector quantization algorithm (VQA) | 0.0012 | 125.53 | 0.584 | 27.39 | 1.23 | 21.4 |

14 | Figure 16b | 0.0048 | 119.51 | 0.512 | 31.25 | 1.36 | 24.5 | |

15 | Figure 16c | 0.0568 | 108.78 | 0.482 | 33.19 | 1.48 | 28.5 | |

16 | Figure 16d | 0.3218 | 101.25 | 0.413 | 38.74 | 1.55 | 31.5 |

S. No | Angle | Generalized Offset | Distance D = 1 | Distance D = 2 | Distance D = 3 | Distance D = 4 | Distance D = 5 | Distance D = 6 | Distance D = 7 | Distance D = 8 |
---|---|---|---|---|---|---|---|---|---|---|

1 | ${0}^{\xb0}$ | $[0,D]$ | $\left[\mathrm{0,1}\right]$ | $\left[\mathrm{0,2}\right]$ | $\left[\mathrm{0,3}\right]$ | $\left[\mathrm{0,4}\right]$ | $\left[\mathrm{0,5}\right]$ | $\left[\mathrm{0,6}\right]$ | $\left[\mathrm{0,7}\right]$ | $\left[\mathrm{0,8}\right]$ |

2 | ${45}^{\xb0}$ | $[-D,D]$ | $[-\mathrm{1,1}]$ | $[-\mathrm{2,2}]$ | $[-\mathrm{3,3}]$ | $[-\mathrm{4,4}]$ | $[-\mathrm{5,5}]$ | $[-\mathrm{6,6}]$ | $[-\mathrm{7,7}]$ | $[-\mathrm{8,8}]$ |

3 | $9{0}^{\xb0}$ | $[-D,0]$ | $[-\mathrm{1,0}]$ | $[-\mathrm{2,0}]$ | $[-\mathrm{3,0}]$ | $[-\mathrm{4,0}]$ | $[-\mathrm{5,0}]$ | $[-\mathrm{6,0}]$ | $[-\mathrm{7,0}]$ | $[-\mathrm{8,0}]$ |

4 | ${135}^{\xb0}$ | $[-D,-D]$ | $[-1,-1]$ | $[-2,-2]$ | $[-3,-3]$ | $[-4,-4]$ | $[-5,-5]$ | $[-6,-6]$ | $[-7,-7]$ | $[-8,-8]$ |

5 | $18{0}^{\xb0}$ | $[0,-D]$ | $[0,-1]$ | $[0,-2]$ | $[0,-3]$ | $[0,-4]$ | $[0,-5]$ | $[0,-6]$ | $[0,-7]$ | $[0,-8]$ |

6 | $225\xb0$ | $[D,-D]$ | $[1,-1]$ | $[2,-2]$ | $[3,-3]$ | $[4,-4]$ | $[5,-5]$ | $[6,-6]$ | $[7,-7]$ | $[8,-8]$ |

7 | $27{0}^{\xb0}$ | $[D,0]$ | $\left[\mathrm{1,0}\right]$ | $\left[\mathrm{2,0}\right]$ | $\left[\mathrm{3,0}\right]$ | $\left[\mathrm{4,0}\right]$ | $\left[\mathrm{5,0}\right]$ | $\left[\mathrm{6,0}\right]$ | $\left[\mathrm{7,0}\right]$ | $\left[\mathrm{8,0}\right]$ |

8 | ${315}^{\xb0}$ | $[D,D]$ | $\left[\mathrm{1,1}\right]$ | $\left[\mathrm{2,2}\right]$ | $\left[\mathrm{3,3}\right]$ | $\left[\mathrm{4,4}\right]$ | $\left[\mathrm{5,5}\right]$ | $\left[\mathrm{6,6}\right]$ | $\left[\mathrm{7,7}\right]$ | $\left[\mathrm{8,8}\right]$ |

**Table 5.**Quantification of the texture features for chest CT scan corresponding to $\mathrm{D}\mathrm{a}\mathrm{y}1$.

Texture | Direction | Change in Distances GLCM Features: (Con. = Contrast), (Corr. = Correlation) | |||||||
---|---|---|---|---|---|---|---|---|---|

GLCM Visual Features | Angle (Degree) | D = 1 | D = 2 | D = 3 | D = 4 | D = 5 | D = 6 | D = 7 | D = 8 |

Con. | ${0}^{\xb0}$ | 0.0619 | 0.1130 | 0.1558 | 0.1945 | 0.2319 | 0.2693 | 0.3080 | 0.3483 |

Corr. | 0.9936 | 0.9883 | 0.9838 | 0.9798 | 0.9759 | 0.9721 | 0.9681 | 0.9639 | |

ASM | 0.1199 | 0.1089 | 0.1010 | 0.0948 | 0.0896 | 0.0851 | 0.0812 | 0.0778 | |

IDM | 0.9691 | 0.9438 | 0.9234 | 0.9058 | 0.8895 | 0.8743 | 0.8597 | 0.8458 | |

Con. | ${45}^{\xb0}$ | 0.0890 | 0.1552 | 0.2125 | 0.2655 | 0.3164 | 0.3673 | 0.4202 | 0.4759 |

Corr. | 0.9907 | 0.9839 | 0.9779 | 0.9724 | 0.9672 | 0.9619 | 0.9565 | 0.9507 | |

ASM | 0.1141 | 0.1019 | 0.0936 | 0.0870 | 0.0817 | 0.0773 | 0.0734 | 0.0701 | |

IDM | 0.9557 | 0.9251 | 0.9012 | 0.8801 | 0.8613 | 0.8437 | 0.8269 | 0.8110 | |

Con. | ${90}^{\xb0}$ | 0.0671 | 0.1216 | 0.1663 | 0.2048 | 0.2391 | 0.2741 | 0.3030 | 0.3349 |

Corr. | 0.9930 | 0.9873 | 0.9827 | 0.9787 | 0.9751 | 0.9718 | 0.9685 | 0.9652 | |

ASM | 0.1192 | 0.1086 | 0.1016 | 0.0964 | 0.0921 | 0.0885 | 0.0853 | 0.0824 | |

IDM | 0.9666 | 0.9411 | 0.9225 | 0.9075 | 0.8944 | 0.8824 | 0.8712 | 0.8605 | |

Con. | ${135}^{\xb0}$ | 0.0934 | 0.1634 | 0.2224 | 0.2762 | 0.3291 | 0.3830 | 0.4395 | 0.4980 |

Corr. | 0.9903 | 0.9830 | 0.9768 | 0.9712 | 0.9657 | 0.9601 | 0.9542 | 0.9481 | |

ASM | 0.1133 | 0.1009 | 0.0926 | 0.0863 | 0.0810 | 0.0765 | 0.0726 | 0.0693 | |

IDM | 0.9535 | 0.9217 | 0.8975 | 0.8766 | 0.8573 | 0.8392 | 0.8218 | 0.8056 |

**Table 6.**Quantification of the texture features for the chest CT scan corresponding to $\mathrm{D}\mathrm{a}\mathrm{y}7$.

Texture | Direction | Change in Distances GLCM Features: (Con. = Contrast), (Corr. = Correlation) | |||||||
---|---|---|---|---|---|---|---|---|---|

GLCM Visual Features | Angle (Degree) | D = 1 | D = 2 | D = 3 | D = 4 | D = 5 | D = 6 | D = 7 | D = 8 |

Con. | ${0}^{\xb0}$ | 0.0730 | 0.1317 | 0.1828 | 0.2308 | 0.2797 | 0.3309 | 0.3843 | 0.4402 |

Corr. | 0.9918 | 0.9852 | 0.9795 | 0.9741 | 0.9686 | 0.9628 | 0.9568 | 0.9505 | |

ASM | 0.1427 | 0.1305 | 0.1220 | 0.1156 | 0.1100 | 0.1050 | 0.1005 | 0.0963 | |

IDM | 0.9635 | 0.9352 | 0.9133 | 0.8951 | 0.8786 | 0.8630 | 0.8484 | 0.8344 | |

Con. | ${45}^{\xb0}$ | 0.1057 | 0.1885 | 0.2613 | 0.3327 | 0.4071 | 0.4849 | 0.5663 | 0.6518 |

Corr. | 0.9881 | 0.9788 | 0.9706 | 0.9625 | 0.9542 | 0.9454 | 0.9362 | 0.9266 | |

ASM | 0.1368 | 0.1239 | 0.1151 | 0.1079 | 0.1018 | 0.0965 | 0.0917 | 0.0875 | |

IDM | 0.9477 | 0.9143 | 0.8890 | 0.8665 | 0.8461 | 0.8273 | 0.8097 | 0.7934 | |

Con. | ${90}^{\xb0}$ | 0.0771 | 0.1431 | 0.1980 | 0.2443 | 0.2874 | 0.3299 | 0.3731 | 0.4173 |

Corr. | 0.9913 | 0.9839 | 0.9777 | 0.9726 | 0.9677 | 0.9630 | 0.9581 | 0.9532 | |

ASM | 0.1437 | 0.1328 | 0.1257 | 0.1203 | 0.1159 | 0.1120 | 0.1085 | 0.1053 | |

IDM | 0.9617 | 0.9337 | 0.9139 | 0.8978 | 0.8836 | 0.8703 | 0.8578 | 0.8461 | |

Con. | ${135}^{\xb0}$ | 0.1060 | 0.1879 | 0.2568 | 0.3229 | 0.3897 | 0.4587 | 0.5309 | 0.6060 |

Corr. | 0.9881 | 0.9789 | 0.9712 | 0.9638 | 0.9563 | 0.9486 | 0.9406 | 0.9322 | |

ASM | 0.1368 | 0.1236 | 0.1150 | 0.1082 | 0.1024 | 0.0974 | 0.0931 | 0.0891 | |

IDM | 0.9477 | 0.9138 | 0.8888 | 0.8672 | 0.8478 | 0.8301 | 0.8140 | 0.7992 |

**Table 7.**Quantification of the texture features for the chest condition corresponding to $\mathrm{D}\mathrm{a}\mathrm{y}14$.

Texture | Direction | Change in Distances GLCM Features: (Con. = Contrast), (Corr. = Correlation) | |||||||
---|---|---|---|---|---|---|---|---|---|

GLCM Visual Features | Angle (Degree) | D = 1 | D = 2 | D = 3 | D = 4 | D = 5 | D = 6 | D = 7 | D = 8 |

Con. | ${0}^{\xb0}$ | 0.0795 | 0.1484 | 0.2106 | 0.2702 | 0.3321 | 0.3977 | 0.4681 | 0.5431 |

Corr. | 0.9902 | 0.9816 | 0.9738 | 0.9663 | 0.9584 | 0.9501 | 0.9412 | 0.9316 | |

ASM | 0.1301 | 0.1150 | 0.1044 | 0.0960 | 0.0891 | 0.0832 | 0.0782 | 0.0738 | |

IDM | 0.9604 | 0.9286 | 0.9030 | 0.8808 | 0.8605 | 0.8418 | 0.8244 | 0.8082 | |

Con. | ${45}^{\xb0}$ | 0.1099 | 0.1958 | 0.2759 | 0.3562 | 0.4396 | 0.5278 | 0.6226 | 0.7234 |

Corr. | 0.9864 | 0.9756 | 0.9654 | 0.9552 | 0.9445 | 0.9331 | 0.9207 | 0.9075 | |

ASM | 0.1235 | 0.1074 | 0.0963 | 0.0876 | 0.0806 | 0.0749 | 0.0700 | 0.0659 | |

IDM | 0.9454 | 0.9073 | 0.8766 | 0.8493 | 0.8247 | 0.8026 | 0.7823 | 0.7638 | |

Con. | ${90}^{\xb0}$ | 0.0744 | 0.1318 | 0.1812 | 0.2271 | 0.2706 | 0.3134 | 0.3557 | 0.3996 |

Corr. | 0.9908 | 0.9837 | 0.9775 | 0.9718 | 0.9663 | 0.9610 | 0.9557 | 0.9501 | |

ASM | 0.1323 | 0.1198 | 0.1113 | 0.1047 | 0.0993 | 0.0946 | 0.0907 | 0.0872 | |

IDM | 0.9628 | 0.9350 | 0.9136 | 0.8953 | 0.8790 | 0.8639 | 0.8502 | 0.8372 | |

Con. | ${135}^{\xb0}$ | 0.1086 | 0.1940 | 0.2730 | 0.3513 | 0.4317 | 0.5169 | 0.6080 | 0.7039 |

Corr. | 0.9865 | 0.9758 | 0.9658 | 0.9558 | 0.9455 | 0.9345 | 0.9227 | 0.9102 | |

ASM | 0.1237 | 0.1075 | 0.0965 | 0.0880 | 0.0811 | 0.0754 | 0.0706 | 0.0666 | |

IDM | 0.9460 | 0.9081 | 0.8776 | 0.8508 | 0.8266 | 0.8041 | 0.7835 | 0.7647 |

**Table 8.**Quantification of the texture features for the chest condition corresponding to $\mathrm{D}\mathrm{a}\mathrm{y}21$.

Texture | Direction | Change in Distance GLCM Features: (Con. = Contrast), (Corr. = Correlation) | |||||||
---|---|---|---|---|---|---|---|---|---|

GLCM Visual Features | Angle (Degree) | D = 1 | D = 2 | D = 3 | D = 4 | D = 5 | D = 6 | D = 7 | D = 8 |

Con. | ${0}^{\xb0}$ | 0.0607 | 0.1054 | 0.1399 | 0.1696 | 0.1967 | 0.2234 | 0.2498 | 0.2764 |

Corr. | 0.9925 | 0.9870 | 0.9827 | 0.9791 | 0.9758 | 0.9725 | 0.9693 | 0.9660 | |

ASM | 0.1651 | 0.1544 | 0.1471 | 0.1416 | 0.1372 | 0.1333 | 0.1299 | 0.1269 | |

IDM | 0.9697 | 0.9476 | 0.9308 | 0.9170 | 0.9052 | 0.8940 | 0.8835 | 0.8737 | |

Con. | ${45}^{\xb0}$ | 0.0899 | 0.1479 | 0.1938 | 0.2351 | 0.2744 | 0.3126 | 0.3516 | 0.3919 |

Corr. | 0.9889 | 0.9817 | 0.9760 | 0.9709 | 0.9661 | 0.9614 | 0.9567 | 0.9517 | |

ASM | 0.1584 | 0.1466 | 0.1392 | 0.1334 | 0.1286 | 0.1246 | 0.1209 | 0.1176 | |

IDM | 0.9551 | 0.9278 | 0.9084 | 0.8919 | 0.8774 | 0.8644 | 0.8517 | 0.8397 | |

Con. | ${90}^{\xb0}$ | 0.0692 | 0.1193 | 0.1588 | 0.1914 | 0.2202 | 0.2473 | 0.2740 | 0.3006 |

Corr. | 0.9914 | 0.9852 | 0.9803 | 0.9763 | 0.9728 | 0.9695 | 0.9662 | 0.9630 | |

ASM | 0.1636 | 0.1528 | 0.1458 | 0.1409 | 0.1369 | 0.1333 | 0.1301 | 0.1272 | |

IDM | 0.9655 | 0.9415 | 0.9244 | 0.9113 | 0.9000 | 0.8896 | 0.8797 | 0.8703 | |

Con. | ${135}^{\xb0}$ | 0.0892 | 0.1469 | 0.1927 | 0.2334 | 0.2712 | 0.3093 | 0.3469 | 0.3862 |

Corr. | 0.9890 | 0.9818 | 0.9762 | 0.9713 | 0.9667 | 0.9621 | 0.9576 | 0.9529 | |

ASM | 0.1586 | 0.1469 | 0.1395 | 0.1338 | 0.1291 | 0.1249 | 0.1215 | 0.1183 | |

IDM | 0.9556 | 0.9285 | 0.9092 | 0.8928 | 0.8781 | 0.8642 | 0.8515 | 0.8393 |

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**MDPI and ACS Style**

Shakya, A.K.; Vidyarthi, A.
Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis. *Technologies* **2024**, *12*, 17.
https://doi.org/10.3390/technologies12020017

**AMA Style**

Shakya AK, Vidyarthi A.
Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis. *Technologies*. 2024; 12(2):17.
https://doi.org/10.3390/technologies12020017

**Chicago/Turabian Style**

Shakya, Amit Kumar, and Anurag Vidyarthi.
2024. "Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis" *Technologies* 12, no. 2: 17.
https://doi.org/10.3390/technologies12020017