An optimized discrete wavelet transform compression technique for image transferring over wireless multimedia sensor network

Transferring images in a wireless multimedia sensor network (WMSN) knows a fast development in both research and fields of application. Nevertheless, this area of research faces many problems such as the low quality of the received images after their decompression, the limited number of reconstructed images at the base station, and the high-energy consumption used in the process of compression and decompression. In order to fix these problems, we proposed a compression method based on the classic discrete wavelet transform (DWT). Our method applies the wavelet compression technique multiple times on the same image. As a result, we found that the number of received images is higher than using the classic DWT. In addition, the quality of the received images is much higher compared to the standard DWT. Finally, the energy consumption is lower when we use our technique. Therefore, we can say that our proposed compression technique is more adapted to the WMSN environment

Compresión Digital en Imágenes Médicas

Imaging technology has long played a principal role in the medical domain, and as such, its use is widespread in the diagnosis and treatment of numerous health conditions. Concurrently, new developments in imaging techniques and sensor technology make possible the acquisition of increasingly detailed images of several organs of the human body. This improvement is indeed advantageous for medical practitioners. However, it comes to a cost in the form of storage and telecommunication infrastructures needed to handle high-resolution images reliably. Ordinarily, digital compression is a mainstay in the efficient management of digital media, including still images and video. From a technical point of view, medical imaging could take full advantage of digital compression technology. However, nuances unique to medical data impose constraints to the application of digital compression in medical images. This paper presents an overview of digital compression in the context of still medical images, along with a brief discussion on related regulatory and legal implications.La Imagenología desempeña un papel protagónico en el campo médico, siendo su uso ampliamente generalizado en el diagnóstico y tratamiento de numerosos trastornos de la salud.Nuevos desarrollos en la adquisición de imágenes y en la tecnología de sensores hacen posible obtener imágenes más detalladas de varios órganos del cuerpo humano. Tal mejora es ciertamente ventajosa para la práctica médica, pero supone un encarecimiento de los recursos tecnológicos necesarios para manejar imágenes de alta resolución de manera confiable. Comúnmente, el manejo eficiente de medios digitales se apoya principalmente en la compresión digital. Desde un punto de vista técnico, las imágenes médicas podrían aprovechar las ventajas de la compresión digital. Sin embargo, peculiaridades de los datos médicos imponen restricciones a su uso. Este artículo presenta un vistazo a la compresión digital en el contexto de las imágenes médicas, y una breve discusión de los aspectos regulatorios y legales asociados a su uso

Computational Dynamic Features Extraction from Anonymized Medical Images

Images depict clearer meaning than written words and this is reason they are used in a variety of human endeavors, including but not limited to medicine. Medical image datasets are used in medical environment to diagnose and confirm medical disorders for which physical examination may not be sufficient. However, the medical profession's ethics of patient confidentiality policy creates barrier to availability of medical datasets for research; thus, this research work was able to solve the above stated barrier through anonymization of sensitive identity information. Furthermore, the Content Based Image Retrieval (CBIR) using texture as the content was developed to overcome the challenge of information overloading associated with data retrieval systems. Images acquired from various imaging modalities and placed into Digital Imaging and Communications in Medicine (DICOM) formats were obtained from several hospitals in Nigeria. The database of these images was created and consequently anonymized, then a new anonymized database was created. On anonymized images, feature extraction was done using textures as content and the content was considered for the implementation of retrieval system. The anonymized images were tested in DICOM view to see if all files were successfully anonymized; the result obtained was 100%. A texture retrieval test was performed, and the number of precisely returned search images using the Similarity Distance Measure formulae resulted in a significant reduction in image overload. Thus, this research work solved the problem of non-availability of datasets for researchers in medical imaging field by providing datasets that can be used without violating law of patient confidentiality by the interested parties. It also solves the problem of hackers obtaining useful information about patients’ datasets. The CBIR using texture as content also enhances and solves the problem of information overloading

An Analysis of Compressive Convolutional Autoencoders for Image Archiving In Medical Informatics

Within a given enterprise network, an array of data types needs to be communicated. These network transmissions consist of images, videos, text, and binaries that have unique requirements of bandwidth and computational overhead to transmit. With respect to medical informatics, these include a multitude of varying subjects, standards, and modalities which are communicated to and from imaging equipment, clinicians, and medical archives. To reduce the required bandwidth to transmit, or provide adequate storage capacity for archival purposes, the data may be compressed in such a way that reduces the size of the image when it is transferred or stored. The original data may be reconstructed either completely or to an acceptable degree of completeness using lossy or lossless compression strategies. The scope of this inquiry is to define ways in which convolutional compressive autoencoders may be used for lossy compression. Multiple approaches will be identified and introduced to define their respective optimal datasets, along with their tuned hyperparameters

Lossless compression methods for magnetic resonance imaging using wavelet transform a systematic review

En medicina la información de las imágenes diagnósticas es vital e imprescindible, por este motivo es necesario procesarlas sin que existan márgenes de error que interfieran con su lectura y análisis. En términos generales: las imágenes presentan redundancia entre píxeles lo cual hace que ocupen un tamaño considerable que va desde los Megabytes (MB) hasta los Gigabytes (GB); el proceso de transmitirlas a través de la red se dificulta en términos de almacenamiento y coste por ende se deben aplicar procesos de compresión sin pérdidas útiles para reducir el ancho de banda, mejorar la capacidad de almacenamiento e incrementar la velocidad de transmisión sin afectar la calidad de la imagen diagnóstica.In medicine, the information from diagnostic images is vital and essential, for this reason, it’s necessary to process them without error margins that could interfere with their reading and analysis. In general terms: images present redundancy between pixels causing them occupy a considerable size ranging from Megabytes (MB) to Gigabytes (GB); the process of transmit them through the network is difficult in terms of storage and computational cost, therefore lossless compression processes must be applied to reduce bandwidth, improve storage capacity and increase transmission speed without affecting the quality of the diagnostic image

Compressão de imagem médica para arquivos de alto desempenho

Information systems and the medical subject are two widespread topics that have interwoven so that medical help could become more efficient. This relation has bred the PACS and the international standard DICOM directed to the organization of digital medical information. The concept of image compression is applied to most images throughout the web. The compression formats used for medical imaging have become outdated. The new formats that have been developed in the past few years are candidates for replacing the old ones in such contexts, possibly enhancing the process. Before they are adopted, an evaluation should be carried out that validates their admissibility. This dissertation reviews the state of the art of medical imaging information systems, namely PACS systems and the DICOM standard. Furthermore, some topics of image compression are covered, such as the metrics for evaluating the algorithms’ performance, finalizing with a survey of four modern formats: JPEG XL, AVIF, and WebP. Two software projects were developed, where the first one carries out an analysis of the formats based on the metrics, using DICOM datasets and producing results that can be used for creating recommendations on the format’s use. The second consists of an application that encodes and decodes medical images with the formats covered in this dissertation. This proof-of-concept works as a medical imaging archive for the storage, distribution, and visualization of compressed data.Os sistemas de informação e o assunto médico são dois temas difundidos que se entrelaçam para que a ajuda médica se torne mais eficiente. Essa relação deu origem ao PACS e ao padrão internacional DICOM direcionado à organização da informação médica digital. O conceito de compressão de imagem é aplicado à maioria das imagens em toda a web. Os formatos de compressão usados para imagens médicas tornaram-se desatualizados. Os novos formatos desenvolvidos nos últimos anos são candidatos a substituir os antigos nesses contextos, possivelmente potencializando o processo. Antes de serem adotados, deve ser realizada uma avaliação que valide sua admissibilidade. Esta dissertação revisa o estado da arte dos sistemas de informação de imagens médicas, nomeadamente os sistemas PACS e a norma DICOM. Além disso, são abordados alguns tópicos de compressão de imagens, como as métricas para avaliação do desempenho dos algoritmos, finalizando com um levantamento de três formatos modernos: JPEG XL, AVIF e WebP. Foram desenvolvidos dois projetos de software, onde o primeiro realiza uma análise dos formatos com base nas métricas, utilizando conjuntos de dados DICOM e produzindo resultados que podem ser utilizados para a criação de recomendações sobre o uso do formato. A segunda consiste numa aplicação capaz de codificar e descodificar imagens médicas com os formatos abordados nesta dissertação. Essa prova de conceito funciona como um arquivo de imagens médicas para armazenamento, distribuição e visualização de dados compactados.Mestrado em Engenharia de Computadores e Telemátic

Prediction-based coding with rate control for lossless region of interest in pathology imaging

Online collaborative tools for medical diagnosis produced from digital pathology images have experimented an increase in demand in recent years. Due to the large sizes of pathology images, rate control (RC) techniques that allow an accurate control of compressed file sizes are critical to meet existing bandwidth restrictions while maximizing retrieved image quality. Recently, some RC contributions to Region of Interest (RoI) coding for pathology imaging have been presented. These encode the RoI without loss and the background with some loss, and focus on providing high RC accuracy for the background area. However, none of these RC contributions deal efficiently with arbitrary RoI shapes, which hinders the accuracy of background definition and rate control. This manuscript presents a novel coding system based on prediction with a novel RC algorithm for RoI coding that allows arbitrary RoIs shapes. Compared to other methods of the state of the art, our proposed algorithm significantly improves upon their RC accuracy, while reducing the compressed data rate for the RoI by 30%. Furthermore, it offers higher quality in the reconstructed background areas, which has been linked to better clinical performance by expert pathologists. Finally, the proposed method also allows lossless compression of both the RoI and the background, producing data volumes 14% lower than coding techniques included in DICOM, such as HEVC and JPEG-LS

The Current Role of Image Compression Standards in Medical Imaging

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings

The Current Role of Image Compression Standards in Medical Imaging

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings