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

Perceptually lossless coding of medical images - from abstraction to reality

This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering images free of visual distortions and at the same time providing significantly greater compression ratio gains over its information lossless counterparts. This is achieved through a visual pruning function, embedded with an advanced model of the human visual system to accurately identify and to efficiently remove visually irrelevant/insignificant information. In addition, it maintains bit-stream compliance with the JPEG 2000 coding framework and subsequently is compliant with the Digital Communications in Medicine standard (DICOM). Equally, the pruning function is applicable to other Discrete Wavelet Transform based image coders, e.g., The Set Partitioning in Hierarchical Trees. Further significant coding gains are exploited through an artificial edge segmentatio n algorithm and a novel arithmetic pruning algorithm. The coding effectiveness and qualitative consistency of the algorithm is evaluated through a double-blind subjective assessment with 31 medical experts, performed using a novel 2-staged forced choice assessment that was devised for medical experts, offering the benefits of greater robustness and accuracy in measuring subjective responses. The assessment showed that no differences of statistical significance were perceivable between the original images and the images encoded by the proposed coder

Techniques for document image processing in compressed domain

The main objective for image compression is usually considered the minimization of storage space. However, as the need to frequently access images increases, it is becoming more important for people to process the compressed representation directly. In this work, the techniques that can be applied directly and efficiently to digital information encoded by a given compression algorithm are investigated. Lossless compression schemes and information processing algorithms for binary document images and text data are two closely related areas bridged together by the fast processing of coded data. The compressed domains, which have been addressed in this work, i.e., the ITU fax standards and JBIG standard, are two major schemes used for document compression. Based on ITU Group IV, a modified coding scheme, MG4, which explores the 2-dimensional correlation between scan lines, is developed. From the viewpoints of compression efficiency and processing flexibility of image operations, the MG4 coding principle and its feature-preserving behavior in the compressed domain are investigated and examined. Two popular coding schemes in the area of bi-level image compression, run-length and Group IV, are studied and compared with MG4 in the three aspects of compression complexity, compression ratio, and feasibility of compressed-domain algorithms. In particular, for the operations of connected component extraction, skew detection, and rotation, MG4 shows a significant speed advantage over conventional algorithms. Some useful techniques for processing the JBIG encoded images directly in the compressed domain, or concurrently while they are being decoded, are proposed and generalized; In the second part of this work, the possibility of facilitating image processing in the wavelet transform domain is investigated. The textured images can be distinguished from each other by examining their wavelet transforms. The basic idea is that highly textured regions can be segmented using feature vectors extracted from high frequency bands based on the observation that textured images have large energies in both high and middle frequencies while images in which the grey level varies smoothly are heavily dominated by the low-frequency channels in the wavelet transform domain. As a result, a new method is developed and implemented to detect textures and abnormalities existing in document images by using polynomial wavelets. Segmentation experiments indicate that this approach is superior to other traditional methods in terms of memory space and processing time

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Compression of Three-Dimensional Magnetic Resonance Brain Images.

Losslessly compressing a medical image set with multiple slices is paramount in radiology since all the information within a medical image set is crucial for both diagnosis and treatment. This dissertation presents a novel and efficient diagnostically lossless compression scheme (predicted wavelet lossless compression method) for sets of magnetic resonance (MR) brain images, which are called 3-D MR brain images. This compression scheme provides 3-D MR brain images with the progressive and preliminary diagnosis capabilities. The spatial dependency in 3-D MR brain images is studied with histograms, entropy, correlation, and wavelet decomposition coefficients. This spatial dependency is utilized to design three kinds of predictors, i.e., intra-, inter-, and intra-and-inter-slice predictors, that use the correlation among neighboring pixels. Five integer wavelet transformations are applied to the prediction residues. It shows that the intra-slice predictor 3 using a x-pixel and a y-pixel for prediction plus the 1st-level (2, 2) interpolating integer wavelet with run-length and arithmetic coding achieves the best compression. An automated threshold based background noise removal technique is applied to remove the noise outside the diagnostic region. This preprocessing method improves the compression ratio of the proposed compression technique by approximately 1.61 times. A feature vector based approach is used to determine the representative slice with the most discernible brain structures. This representative slice is progressively encoded by a lossless embedded zerotree wavelet method. A rough version of this representative slice is gradually transmitted at an increasing bit rate so the validity of the whole set can be determined early. This feature vector based approach is also utilized to detect multiple sclerosis (MS) at an early stage. Our compression technique with the progressive and preliminary diagnosis capability is tested with simulated and real 3-D MR brain image sets. The compression improvement versus the best commonly used lossless compression method (lossless JPEG) is 41.83% for simulated 3-D MR brain image sets and 71.42% for real 3-D MR brain image sets. The accuracy of the preliminary MS diagnosis is 66.67% based on six studies with an expert radiologist\u27s diagnosis

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

Efficient interaction with large medical imaging databases

Everyday, a wide quantity of hospitals and medical centers around the world are producing large amounts of imaging content to support clinical decisions, medical research, and education. With the current trend towards Evidence-based medicine, there is an increasing need of strategies that allow pathologists to properly interact with the valuable information such imaging repositories host and extract relevant content for supporting decision making. Unfortunately, current systems are very limited at providing access to content and extracting information from it because of different semantic and computational challenges. This thesis presents a whole pipeline, comprising 3 building blocks, that aims to to improve the way pathologists and systems interact. The first building block consists in an adaptable strategy oriented to ease the access and visualization of histopathology imaging content. The second block explores the extraction of relevant information from such imaging content by exploiting low- and mid-level information obtained from from morphology and architecture of cell nuclei. The third block aims to integrate high-level information from the expert in the process of identifying relevant information in the imaging content. This final block not only attempts to deal with the semantic gap but also to present an alternative to manual annotation, a time consuming and prone-to-error task. Different experiments were carried out and demonstrated that the introduced pipeline not only allows pathologist to navigate and visualize images but also to extract diagnostic and prognostic information that potentially could support clinical decisions.Resumen: Diariamente, gran cantidad de hospitales y centros médicos de todo el mundo producen grandes cantidades de imágenes diagnósticas para respaldar decisiones clínicas y apoyar labores de investigación y educación. Con la tendencia actual hacia la medicina basada en evidencia, existe una creciente necesidad de estrategias que permitan a los médicos patólogos interactuar adecuadamente con la información que albergan dichos repositorios de imágenes y extraer contenido relevante que pueda ser empleado para respaldar la toma de decisiones. Desafortunadamente, los sistemas actuales son muy limitados en cuanto al acceso y extracción de contenido de las imágenes debido a diferentes desafíos semánticos y computacionales. Esta tesis presenta un marco de trabajo completo para patología, el cual se compone de 3 bloques y tiene como objetivo mejorar la forma en que interactúan los patólogos y los sistemas. El primer bloque de construcción consiste en una estrategia adaptable orientada a facilitar el acceso y la visualización del contenido de imágenes histopatológicas. El segundo bloque explora la extracción de información relevante de las imágenes mediante la explotación de información de características visuales y estructurales de la morfología y la arquitectura de los núcleos celulares. El tercer bloque apunta a integrar información de alto nivel del experto en el proceso de identificación de información relevante en las imágenes. Este bloque final no solo intenta lidiar con la brecha semántica, sino que también presenta una alternativa a la anotación manual, una tarea que demanda mucho tiempo y es propensa a errores. Se llevaron a cabo diferentes experimentos que demostraron que el marco de trabajo presentado no solo permite que el patólogo navegue y visualice imágenes, sino que también extraiga información de diagnóstico y pronóstico que potencialmente podría respaldar decisiones clínicas.Doctorad