306 research outputs found
Lossless compression of medical images
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1991.Thesis (Master's) -- Bilkent University, 1991.Includes bibliographical references.The digital imaging techniques are used more and more in various diagnostic
modalities, including computed tomography (CT), Digital Subtraction
.A.ngiography (DSA), etc. As a result, a huge amount of digital images are
being generated. Therefore, techniques to compress these images into a more
compact form for storage and transmission become more important and anyone
involved with the storage of medical images in a Picture Archiving and Communication
System (PACS) should first apply compression (lossy or lossless
depending on the characteristics of the digitized data) on them. There are image
coding methods which reduces the storage size of an image b}' an amount of
1/10 without any visual degradation. But in some medical applications lossless
coding of medical images is necessary.
Multiresolution coding techniques have been used for lossy image and speech
coding, in this thesis we developed multiresolution techniques for the lossless
image compression. We observed that the use of multiresolution techniques in
lossless compression is as advantages as in lossy compression schemes.Kılıç, Behiç FıratM.S
Selective Compression of Medical Images via Intelligent Segmentation and 3D-SPIHT Coding
ABSTRACT
SELECTIVE COMPRESSION OF MEDICAL IMAGES VIA INTELLIGENT SEGMENTATION AND 3D-SPIHT CODING
by
Bohan Fan
The University of Wisconsin-Milwaukee, 2018
Under the Supervision of Professor Zeyun Yu
With increasingly high resolutions of 3D volumetric medical images being widely used in clinical patient treatments, efficient image compression techniques have become in great demand due to the cost in storage and time for transmission. While various algorithms are available, the conflicts between high compression rate and the downgraded quality of the images can partially be harmonized by using the region of interest (ROI) coding technique. Instead of compressing the entire image, we can segment the image by critical diagnosis zone (the ROI zone) and background zone, and apply lossless compression or low compression rate to the former and high compression rate to the latter, without losing much clinically important information.
In this thesis, we explore a medical image transmitting process that utilizes a deep learning network, called 3D-Unet to segment the region of interest area of volumetric images and 3D-SPIHT algorithm to encode the images for compression, which can be potentially used in medical data sharing scenario. In our experiments, we train a 3D-Unet on a dataset of spine images with their label ground truth, and use the trained model to extract the vertebral bodies of testing data. The segmented vertebral regions are dilated to generate the region of interest, which are subject to the 3D-SPIHT algorithm with low compress ratio while the rest of the image (background) is coded with high compress ratio to achieve an excellent balance of image quality in region of interest and high compression ratio elsewhere
Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network
Deep learning-based lossless compression methods offer substantial advantages
in compressing medical volumetric images. Nevertheless, many learning-based
algorithms encounter a trade-off between practicality and compression
performance. This paper introduces a hardware-friendly streaming lossless
volumetric compression framework, utilizing merely one-thousandth of the model
weights compared to other learning-based compression frameworks. We propose a
gated recurrent convolutional neural network that combines diverse
convolutional structures and fusion gate mechanisms to capture the inter-slice
dependencies in volumetric images. Based on such contextual information, we can
predict the pixel-by-pixel distribution for entropy coding. Guided by
hardware/software co-design principles, we implement the proposed framework on
Field Programmable Gate Array to achieve enhanced real-time performance.
Extensive experimental results indicate that our method outperforms traditional
lossless volumetric compressors and state-of-the-art learning-based lossless
compression methods across various medical image benchmarks. Additionally, our
method exhibits robust generalization ability and competitive compression speedComment: 18 pages, 8 figure
Application of Novel Lossless Compression of Medical Images Using Prediction and Contextual Error Modeling
Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest
possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with
high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a
novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication
and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the
local image region. The main contribution of this work is the enhancement of the well known approach of predictor
blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique.
We show that this approach is well suited for medical image data compression. Results obtained with the proposed
compression method on medical images are very encouraging, beating several well known lossless compression methods.
The predictor proposed can also be used in other image processing applications such as segmentation and extraction of
image regions
Application of Novel Lossless Compression of Medical Images Using Prediction and Contextual Error Modeling
Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest
possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with
high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a
novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication
and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the
local image region. The main contribution of this work is the enhancement of the well known approach of predictor
blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique.
We show that this approach is well suited for medical image data compression. Results obtained with the proposed
compression method on medical images are very encouraging, beating several well known lossless compression methods.
The predictor proposed can also be used in other image processing applications such as segmentation and extraction of
image regions
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
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