La mayoría de los navegadores no permiten la visualización de imágenes DICOM, requiriendo la instalación de alguna aplicación en el cliente Web. Para lo cual, se han desarrollado Web-PACS que posibilitan la visualización de estos estudios mediante el uso de navegadores Web. Existe un número creciente de aplicaciones que requieren la transmisión remota de las imágenes generadas con fines de diagnóstico. Para la gestión y distribución de las imágenes en formato Digital Imaging and Communication in Medicine (DICOM), las instituciones de salud recurren a la utilización del Picture Archiving and Communication Systems (PACS). The compressed image can be accessed and sent over telemedicine network using personal digital assistance (PDA) like mobile.Įn la actualidad los dispositivos de captura de modalidades médicas llegan a generar imágenes digitales de gran resolución o estudios con múltiples cortes que pueden ocupar algunos gigabytes. The main objective of this work is to reject the noisy background and reconstruct the image portions losslessly. In this paper, we propose lossless scalable RBC for Digital Imaging and Communications in Medicine (DICOM) images based on Integer Wavelet Transform (IWT) and with distortion limiting compression technique for other regions in image. Lossless compression schemes with secure transmission play a key role in telemedicine applications that help in accurate diagnosis and research. Hence, Region Based Coding (RBC) technique is significant for medical image compression and transmission. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Compression methods capable of delivering higher reconstruction quality for important parts are attractive in this situation. Many classes of images contain spatial regions which are more important than other regions. We compare the performing of SVD with DCT and wavelets and show the results.
This algorithm turns out to be well suited for progressive transmission and ROI selection of 2D and 3D images, as it is able to avoid redundancy in data transmission and does not require any sort of data recodification, even if we select arbitrary ROIs on the fly. In this context we present an algorithm for lossy adaptive encoding based on singular value decomposition (SVD). In the progressive transmission of ROIs, we want not only to reconstruct the image as we receive image data, but also to be able to select which part or parts of the emerging image we think are relevant and want to receive first, and which part or parts are of no interest. However, none of them is well suited, or perform poorly, when, in addition to progressive transmission, we want to include also ROIs (Region Of Interest) handling. There are many progressive transmission methods available, such as it planes, TSVQ, DPCM, and, more recently, matrix polynomial interpolation, Discrete Cosine Transform (DCT, used in JPEG) and wavelets (used in JPEG 2000). Despite this reconstruction being, of course, partial, it is possible to improve the reconstruction on the fly, as more and more data of the original image are received. These schemes allow the image sender to encode the image data in such a way that it is possible for the receiver to perform a reconstruction of the original image from the very beginning of transmission. To solve this issue, progressive transmission schemes are used. dial-up or mobile network), because the receiver must wait until the entire image has arrived. medical image such as Computed Tomographies or satellite images) of 10, 50, 100 or more Megabytes, due to the amount of time required for transmitting and displaying, this time being even worse when a narrow bandwidth transmission medium is involved (i.e. Nowadays, problems arise when handling large-sized images (i.e.
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