# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 3 Proposed Methodology
### Proposed Algorithm  
The clinical data repository combines statistics from various clinical resources, including EMR or laboratory systems, to provide a complete picture of a patient's care. These archives are characterized as databases containing scientific statistics. Healthcare extensive records is a term used to describe the large number of records created by adopting digital technologies that collect patient statistics and help manage healthcare services that are otherwise too large and complex for classic technologies. Clinical repositories can provide a rich overview of patients, their clinical conditions, and outcomes. The database can provide a way to explore associations and capacity styles between disease progression and treatment. The proposed algorithm with improved embedding is sketched in Algorithm 1. The DWT coefficients were first tested (approximate and detail coefficients are processed separately) by inserting zeros between each coefficient, doubling each length exactly. Just as the concept of filter financial institutions can determine DWT, so can IDWT reconstruction be performed by taking the real first N/2-1 coefficients of the DWT coefficients and adding them to the transfer. RMSE suggests that square deviation is one of the most typically used measures for comparing excellent predictions. It shows how some distance predictions fall from measured actual values using Euclidean distance. To compute RMSE, calculate the residual (the distinction between prediction and fact) for each statistical factor, compute the residual norm for every information factor, compute the suggestion of residuals, and take the square root of that mean. RMSE is typically utilized in supervised mastering applications, as RMSE uses and requires accurate measurements for each predicted record factor. ```
1: Load the medical image and the patient data as X \(\leftarrow\) medical image and D \(\leftarrow\) patient data. 2: Adjust the matrix dimensions such that the rows are multiples of 3 and the columns of 2. 3: Pad the extra row or column with the value '256' as the pixel ranges between 0 and 255. 4: Extract the ASCII value of D and convert it to its equivalent binary value as D \(\rightarrow\) ASCII (D) \(\rightarrow\) Binary(D). 5: Apply KT for pixel traversing the image in the 'L' path, and change the LSB bit of each pixel with each bit of the binary value from D.
6: Delete the extra padded row or column. 7: Extract the red, green, and blue components from X. 8: Apply forward DWT for the red, green, and blue components using the formula

\[I_{DWT}(a,b)=2^{-a/2}\int_{-\infty}^{+\infty}i(t)\psi\big{(}2^{-a}t-b\big{)}dt \tag{1}\]

\[\psi_{a,b}(t)=2^{-a/2}\psi\big{(}2^{-a}t-b\big{)} \tag{2}\]

where \(\psi\)(t) is the mother wavelet. 9: Combine the individual transformed components into one matrix. trans = combine (red, blue, and green)
10: Compute lossless wavelet packet compression using 'wpdencmp' function with 'haar' wavelet packet and store it in the repository. Z = compress (wpdencmp, trans, haar)The extraction algorithm is defined in Algorithm 2 as follows. ```
1: Retrieve the compressed image from the medical database and perform wavelet decompression. trans = decompress (Z)
2: Apply Inverse DWT (IDWT). \[i(t)=\sum_{a}\sum_{b}I_{DWT}(a,b)\left[2^{-a/2}\psi\left(2^{-a}t-b\right)\right]\] (3)
3: Extract the red, blue, and green components from the reconstructed image, and combine them. 4: Make the image matrix traversable by adding an extra row or column and pad with the value '256'. 5: Apply the KT to extract the LSB values from the pixels of the image traversing in an 'L' pattern. 6: Delete the extra padded row or column. 7: Convert the obtained binary bits to ASCII values and the character. Binary (D) = ASCII (D) = char (D) = D
8: Update the patient data = D and medical image = X. ```
**Algorithm 2** Extraction

The proposed image compression is defined in Algorithm 3.