Medical image compression based on vector quantization and discrete wavelet transform

Due to the prevalence of chronic diseases, diagnostic imaging procedures are increasing annually. However, storage is challenging because storage capacity is limited to accommodate these ever-increasing medical images. As a result, it has become important to develop new ways to compress these images due to their large sizes. Therefore, there is a need for a compression method that preserves medical diagnoses and has a high compression ratio. Image compression aims to significantly reduce the size of different types of images while preventing distortion and harmful distortion during reconstruction. The medical image compression system is an important topic in image processing systems to reliably compress and decompress various types of medical images since image compression has become the latest emerging trend all over the world. This can be accurately implemented for both lossy and transparency techniques, such as discrete waveform transform (DWT), discrete cosine transform (DCT), fractal-based and wavelet-based notation vector quantization (VQ), predictive coding, and so on. Although these techniques are used to design the compression system, they face some challenges, such as reducing the original size, level of computational complexity, and minimum square of compression errors. This thesis addresses these critical challenges to improve medical image compression performance. There are two main ways to compress images, each of which is determined by whether or not the original image can be reconstructed from the compressed image. Lossless compression is where no information is lost in compression or decompression operations, and this is used with medical images to keep all image information. Lossy compression is where the entire image cannot be decompressed because it uses a lossy compression method where some information has to be deleted. To compress digital images without losing quality, they are widely used along with JPEG. The purpose of this thesis is to introduce a simple and effective image compression technique that increases the efficiency of the compression system and improves the performance of compression methods based on discrete waveform transform (DWT) while maintaining the mean structural similarity (MSSIM) and Peak signal-to-noise ratio (PSNR) at an acceptable level. Medical images represented by MRI, CT, and ultrasound are the data sources for this thesis. We used MATLAB (R2022b) to formulate the necessary coding. To accomplish this work and to take advantage of the capabilities of the program in the field of image processing, this thesis proposes a combination of Discrete Wavelet Transform (DWT) and Vector Quantization (VQ) to process complex medical images while preserving the diagnostic content. Medical images inevitably suffer from salt and pepper noise; in this method, filtering removes them during pre-processing. We use [3×3] Gaussian algorithm to determine the value of each pixel in the output image based on the value of its associated pixel and adjacent pixels in the input image. Then wavelet analysis (DWT) can divide image information into approximate and detailed partial signals. A close-up sub-signal shows the general direction of the pixel value, and three detailed sub-signals show vertical, horizontal, and diagonal details or changes in the image. If this detail is small, it can be set to zero without significantly changing the image. There are two types of filters, high pass filter and low pass filter. Thus, the signal is effectively divided into a detailed high-frequency part and a rough low-frequency part. A Discrete Wavelet Transform (DWT) is applied to preserve the edges of the images (horizontal, vertical and diagonal) as the image is divided into four bands, and the sub-band conversion is repeated with the highest level of specificity. Vector Quantization (VQ) technique is used for data compression, correction, pattern recognition, density and clustering estimation. Missing data from some ranges is retrieved by finding the closest set, including the available data ranges. It will get the same value as the midpoint VQ applied to all the parameters generated by the previous step except for the sub-range. This contains the most important details for image recovery since the basis of VQ work is to represent pixel arrays by an index in the codebook. Many improvements are made to reduce the computation time for image compression. The experiments performed at this point are different codebook sizes within different-sized windows for each image. Performance efficiency was evaluated with different sizes of codebooks and windows and for increasing the pressure by arithmetic coding. Then we use the threshold principle between the two methods that generate wave coefficients and exclude the value less than the specified threshold. The best combination of hybrid technology was found at two levels of DWT before quantification. This thesis tested the images by applying three separate analytical levels of the wavelet transform. The resulting wave coefficients were then encoded using image coding techniques based on wavelet transform, which is arithmetic coding. With this proposed hybrid technology, we achieve a good performance, and by adding an operation optimization algorithm, we achieve even better performance. The proposed compression scheme achieved better results than the referenced studies and increased the compression ratio. The test showed that decomposing the wave into three levels achieved the best compression ratio and the best encoding technique used with the previous wave. Several factors affect the results of the previous tests, the most important of which are the type of encoding technique for wavelet coefficients and the optimization algorithm used. Tests showed that decomposing the wavelets into three levels achieved the best compression ratio and the best encoding technique used with the previous wave. To compress images, we use the arithmetic coding (AC) algorithm, which uses fractions between 0 and 1 to generate a unique string of characters. A data code encoding and decoding algorithm repeatedly processes and encodes (decodes) a single data code, making it symbolically iterative. When the algorithm iterates, it divides the period between 0 and 1 into smaller and smaller parts, keeping one of these pieces as a new period. The technology runs on layered time slots, each of which contains a sequence of code that is evaluated for size. Code stream size comparisons are used to reconstruct the data stream, indicating how the encoder divides and stores overlapping subintervals, unlike well-known compression methods such as prefix Huffman symbols. Then we turn to artificial intelligence, using the particle swarm optimization (PSO) algorithm. The particle swarm development method is almost certain to find a globally optimal solution. The global search ability is also great, and the computational performance is much faster than traditional methods. The test was done using the Compression Ratio (CR) parameter, the Mean Square Error (MSR) parameter, and the Peak Signal to Noise Ratio (PSNR) parameter. Experiments were performed with different codebook sizes and window sizes for each image. A test was performed to evaluate the efficiency of the measuring tool with different sizes of codebooks and windows. Experiments at both levels confirm the larger size of the codebooks. The best are chosen to maintain image quality and thus a suitable choice of the hybrid algorithm because image quality is critical in medical imaging. At the end of the work, we compared it with a reference study where the fine image details were used in terms of extension, dimensions, and pixel depth, which gave better results than the referenced studies and increased the compression ratio. It was found that applying the proposed scheme to compress images using optimization algorithms helps to obtain the best results Providing several proposals to take into account the time and improve performance.