A novel approach and implementation of discrete wavelets transform (DWT) for clustering and compression of kidney disease images

Kidney disease posed a major problem in the field of medicine in terms of the inability to limit the location of the disease in the diseased kidney. To solve this problem, the clustering technique was applied to the image of the diseased kidney to find the value of k in the theory of Clustering With K-means using the MATLAB program. To improve the results in this work, it was derived A new technique is to create a new filter derived from the first Chebyshev polynomials using high-tech mathematical methods to obtain into New Discrete First Chebyshev Wavelets Transform (NDFCWT). The large data contained in the kidney sample, and to reduce the areas exploited by this data, the image was compressed, and after the completion of the compression process, the image quality standards are recorded, which are peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP). The study comprehensively evaluates its performance through extensive computational experiments across multiple datasets, including high-dimensional, noisy, and complex data structures. The results are compared before and after using the proposed technique, MSE before used new method 5.131. However, after used new method which resulted in improved results in obtaining value K-means is 0.950. The proposed approach, demonstrates significant improvements in clustering accuracy, computational efficiency, and noise resistance compared to existing wavelet transform techniques. Establishment of the mathematical formulation of the new discrete first Chebyshev wavelet transform (NDFCWT) with the ability to process digital image signals, The NDFCWT achieves superior performance through different calculations compared to existing wavelets, detailed insights into the algorithmic mechanics are the scientific and mathematical foundations of the proposed method