CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images

Medical image compression is a widely studied field of data processing due to its prevalence in modern digital databases. This domain requires a high color depth of 12 bits per pixel component for accurate analysis by physicians, primarily in the DICOM format. Standard raster-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations. This study proposes a lossless medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing. The algorithm employs fractal pixel traversal coupled with a novel approach of segmentation and meshing between pixel blocks for preprocessing. Furthermore, delta and entropy coding are applied to this concept for a complete compression pipeline. The proposal demonstrates that the data compression achieved via fractal segmentation preprocessing yields enhanced image compression results while remaining lossless in its reconstruction accuracy. CompaCT is evaluated in its compression ratios on 3954 high-color CT scans against the efficiency of industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its reconstruction performance is assessed with error metrics to verify lossless image recovery after decompression. The results demonstrate that CompaCT can compress and losslessly reconstruct medical images, being 37% more space-efficient than industry-standard compression systems.Comment: (8/24/2023) v1a: 16 pages, 9 figures, Word PD

JPEG2000 ROI coding through component priority for digital mammography

Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. - This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding. The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component support of JPEG2000 codestream. The main insight in ROITCOP is the allocation of each ROI to an component. Experimental results indicate that this ROI allocation strategy does not penalize coding performance whilst achieving an unprecedented degree of accuracy to delimit ROIs. - The proposed ROITCOP method maintains JPEG2000 compliance, thus easing its use in medical centers to share images. This paper analyzes in detail the use of ROITCOP to mammographies, where the ROIs are identified by computer-aided diagnosis. Extensive experimental tests using various ROI coding methods suggest that ROITCOP achieves enhanced coding performanc

JPEG2000 ROI coding through component priority for digital mammography

Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. - This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding. The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component support of JPEG2000 codestream. The main insight in ROITCOP is the allocation of each ROI to an component. Experimental results indicate that this ROI allocation strategy does not penalize coding performance whilst achieving an unprecedented degree of accuracy to delimit ROIs. - The proposed ROITCOP method maintains JPEG2000 compliance, thus easing its use in medical centers to share images. This paper analyzes in detail the use of ROITCOP to mammographies, where the ROIs are identified by computer-aided diagnosis. Extensive experimental tests using various ROI coding methods suggest that ROITCOP achieves enhanced coding performanc

Implementation of Transform Based Techniques in Digital Image Watermarking

Digital image watermarking is used to resolve the problems of data security and copyright protection. In many applications of digital watermarking, watermarked image of good quality are required. But here is a trade-off between number of embedded watermark images and quality of watermarked images. This aspect is quite important in case of multiple digital image watermarking. This project presents a robust digital image watermarking using discrete cosine transform (DCT) method. Compression on a watermarked image can significantly affect the detection of the embedded watermark. The detection of the presence or absence of a watermarked in an image is often affected if the watermarked image has undergone compression. Compression can also be considered as an attack on watermarked images. To show that a particular watermarking scheme is robust against compression, simulation is often relied DOI: 10.17762/ijritcc2321-8169.15084

Image Compression Based On Region Of Interest For Computerized Tomography Images

The use of computers for handling image data in the healthcare is growing. The amount of data produced by modem image generating techniques, such as Computed Tomography (CT) and Magnetic Resonance (MR), is vast. The amount of data might be a problem from a storage point of view or when the data is sent over a network. To overcome these problems data compression techniques adapted to these applications are needed. Many classes of images contain some spatial regions which are more important than other regions. Compression methods which are capable of achieving higher reconstruction quality of important parts of the image have been implemented. For medical images, only a small portion of the image might be diagnostically useful, but the cost of wrong interpretation is high. Algorithms which deliver lossless compression within the regions of interest (ROI), and lossy compression elsewhere in the image, might be the key to providing efficient and accurate image coding to the medical community. In this thesis both of compression techniques (lossy and lossless) of medical images using the JPEG algorithm (DCT), will be discussed

Astronomical Image Compression Techniques Based on ACC and KLT Coder

This paper deals with a compression of image data in applications in astronomy. Astronomical images have typical specific properties — high grayscale bit depth, size, noise occurrence and special processing algorithms. They belong to the class of scientific images. Their processing and compression is quite different from the classical approach of multimedia image processing. The database of images from BOOTES (Burst Observer and Optical Transient Exploring System) has been chosen as a source of the testing signal. BOOTES is a Czech-Spanish robotic telescope for observing AGN (active galactic nuclei) and the optical transient of GRB (gamma ray bursts) searching. This paper discusses an approach based on an analysis of statistical properties of image data. A comparison of two irrelevancy reduction methods is presented from a scientific (astrometric and photometric) point of view. The first method is based on a statistical approach, using the Karhunen-Loeve transform (KLT) with uniform quantization in the spectral domain. The second technique is derived from wavelet decomposition with adaptive selection of used prediction coefficients. Finally, the comparison of three redundancy reduction methods is discussed. Multimedia format JPEG2000 and HCOMPRESS, designed especially for astronomical images, are compared with the new Astronomical Context Coder (ACC) coder based on adaptive median regression

Multi-regional Adaptive Image Compression (AIC) for hip fractures in pelvis radiography

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content

Perceptual lossless medical image coding

A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder

Preserving data integrity of encoded medical images: the LAR compression framework

International audienceThrough the development of medical imaging systems and their integration into a complete information system, the need for advanced joint coding and network services becomes predominant. PACS (Picture Archiving and Communication System) aims to acquire, store and compress, retrieve, present and distribute medical images. These systems have to be accessible via the Internet or wireless channels. Thus protection processes against transmission errors have to be added to get a powerful joint source-channel coding tool. Moreover, these sensitive data require confidentiality and privacy for both archiving and transmission purposes, leading to use cryptography and data embedding solutions. This chapter introduces data integrity protection and developed dedicated tools of content protection and secure bitstream transmission for medical encoded image purposes. In particular, the LAR image coding method is defined together with advanced securization services

Three-dimensional adaptive image compression concept for medical imaging : application to computed tomography angiography for peripheral arteries

Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information