Standard and specific compression techniques for DNA microarray images

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato's CNN-based scheme

Lossy-to-Lossless Compression of Biomedical Images Based on Image Decomposition

The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). Microarray imaging and images that can be extracted from RNA interference (RNAi) experiments also play an important role for large-scale gene sequence and gene expression analysis, allowing the study of gene function, regulation, and interaction across a large number of genes and even across an entire genome. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted at the highest possible fidelity between various hospitals, medical organizations, or research units

Lossless compression of images with specific characteristics

Doutoramento em Engenharia ElectrotécnicaA compressão de certos tipos de imagens é um desafio para algumas normas de compressão de imagem. Esta tese investiga a compressão sem perdas de imagens com características especiais, em particular imagens simples, imagens de cor indexada e imagens de microarrays. Estamos interessados no desenvolvimento de métodos de compressão completos e no estudo de técnicas de pré-processamento que possam ser utilizadas em conjunto com as normas de compressão de imagem. A esparsidade do histograma, uma propriedade das imagens simples, é um dos assuntos abordados nesta tese. Desenvolvemos uma técnica de pré-processamento, denominada compactação de histogramas, que explora esta propriedade e que pode ser usada em conjunto com as normas de compressão de imagem para um melhoramento significativo da eficiência de compressão. A compactação de histogramas e os algoritmos de reordenação podem ser usados como préprocessamento para melhorar a compressão sem perdas de imagens de cor indexada. Esta tese apresenta vários algoritmos e um estudo abrangente dos métodos já existentes. Métodos específicos, como é o caso da decomposição em árvores binárias, são também estudados e propostos. O uso de microarrays em biologia encontra-se em franca expansão. Devido ao elevado volume de dados gerados por experiência, são necessárias técnicas de compressão sem perdas. Nesta tese, exploramos a utilização de normas de compressão sem perdas e apresentamos novos algoritmos para codificar eficientemente este tipo de imagens, baseados em modelos de contexto finito e codificação aritmética.The compression of some types of images is a challenge for some standard compression techniques. This thesis investigates the lossless compression of images with specific characteristics, namely simple images, color-indexed images and microarray images. We are interested in the development of complete compression methods and in the study of preprocessing algorithms that could be used together with standard compression methods. The histogram sparseness, a property of simple images, is addressed in this thesis. We developed a preprocessing technique, denoted histogram packing, that explores this property and can be used with standard compression methods for improving significantly their efficiency. Histogram packing and palette reordering algorithms can be used as a preprocessing step for improving the lossless compression of color-indexed images. This thesis presents several algorithms and a comprehensive study of the already existing methods. Specific compression methods, such as binary tree decomposition, are also addressed. The use of microarray expression data in state-of-the-art biology has been well established and due to the significant volume of data generated per experiment, efficient lossless compression methods are needed. In this thesis, we explore the use of standard image coding techniques and we present new algorithms to efficiently compress this type of images, based on finite-context modeling and arithmetic coding

Algoritmos de compressão sem perdas para imagens de microarrays e alinhamento de genomas completos

Doutoramento em InformáticaNowadays, in the 21st century, the never-ending expansion of information is a major global concern. The pace at which storage and communication resources are evolving is not fast enough to compensate this tendency. In order to overcome this issue, sophisticated and efficient compression tools are required. The goal of compression is to represent information with as few bits as possible. There are two kinds of compression, lossy and lossless. In lossless compression, information loss is not tolerated so the decoded information is exactly the same as the encoded one. On the other hand, in lossy compression some loss is acceptable. In this work we focused on lossless methods. The goal of this thesis was to create lossless compression tools that can be used in two types of data. The first type is known in the literature as microarray images. These images have 16 bits per pixel and a high spatial resolution. The other data type is commonly called Whole Genome Alignments (WGA), in particularly applied to MAF files. Regarding the microarray images, we improved existing microarray-specific methods by using some pre-processing techniques (segmentation and bitplane reduction). Moreover, we also developed a compression method based on pixel values estimates and a mixture of finite-context models. Furthermore, an approach based on binary-tree decomposition was also considered. Two compression tools were developed to compress MAF files. The first one based on a mixture of finite-context models and arithmetic coding, where only the DNA bases and alignment gaps were considered. The second tool, designated as MAFCO, is a complete compression tool that can handle all the information that can be found in MAF files. MAFCO relies on several finite-context models and allows parallel compression/decompression of MAF files.Hoje em dia, no século XXI, a expansão interminável de informação é uma grande preocupação mundial. O ritmo ao qual os recursos de armazenamento e comunicação estão a evoluir não é suficientemente rápido para compensar esta tendência. De forma a ultrapassar esta situação, são necessárias ferramentas de compressão sofisticadas e eficientes. A compressão consiste em representar informação utilizando a menor quantidade de bits possível. Existem dois tipos de compressão, com e sem perdas. Na compressão sem perdas, a perda de informação não é tolerada, por isso a informação descodificada é exatamente a mesma que a informação que foi codificada. Por outro lado, na compressão com perdas alguma perda é aceitável. Neste trabalho, focámo-nos apenas em métodos de compressão sem perdas. O objetivo desta tese consistiu na criação de ferramentas de compressão sem perdas para dois tipos de dados. O primeiro tipo de dados é conhecido na literatura como imagens de microarrays. Estas imagens têm 16 bits por píxel e uma resolução espacial elevada. O outro tipo de dados é geralmente denominado como alinhamento de genomas completos, particularmente aplicado a ficheiros MAF. Relativamente às imagens de microarrays, melhorámos alguns métodos de compressão específicos utilizando algumas técnicas de pré-processamento (segmentação e redução de planos binários). Além disso, desenvolvemos também um método de compressão baseado em estimação dos valores dos pixéis e em misturas de modelos de contexto-finito. Foi também considerada, uma abordagem baseada em decomposição em árvore binária. Foram desenvolvidas duas ferramentas de compressão para ficheiros MAF. A primeira ferramenta, é baseada numa mistura de modelos de contexto-finito e codificação aritmética, onde apenas as bases de ADN e os símbolos de alinhamento foram considerados. A segunda, designada como MAFCO, é uma ferramenta de compressão completa que consegue lidar com todo o tipo de informação que pode ser encontrada nos ficheiros MAF. MAFCO baseia-se em vários modelos de contexto-finito e permite compressão/descompressão paralela de ficheiros MAF

Adaptive edge-based prediction for lossless image compression

Many lossless image compression methods have been suggested with established results hard to surpass. However there are some aspects that can be considered to improve the performance further. This research focuses on two-phase prediction-encoding method, separately studying each and suggesting new techniques.;In the prediction module, proposed Edge-Based-Predictor (EBP) and Least-Squares-Edge-Based-Predictor (LS-EBP) emphasizes on image edges and make predictions accordingly. EBP is a gradient based nonlinear adaptive predictor. EBP switches between prediction-rules based on few threshold parameters automatically determined by a pre-analysis procedure, which makes a first pass. The LS-EBP also uses these parameters, but optimizes the prediction for each pre-analysis assigned edge location, thus applying least-square approach only at the edge points.;For encoding module: a novel Burrows Wheeler Transform (BWT) inspired method is suggested, which performs better than applying the BWT directly on the images. We also present a context-based adaptive error modeling and encoding scheme. When coupled with the above-mentioned prediction schemes, the result is the best-known compression performance in the genre of compression schemes with same time and space complexity

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression