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In this ®gure, the top row of images shows chest radiographs compressed (using SPIHT wavelet method) at 10:1, 50:1, and 200:1. A magni®ed subregion is shown in the upper right corner. The absolute error image, using a display
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The volume of data from medical imaging is growing at exponential rates, matching or exceeding the decline in the costs of digital data storage. While methods to reversibly compress image data do exist, current methods only achieve modest reductions in storage requirements. Irreversible compression can achieve substantially higher compression ratio...
Context in source publication
Context 1
... low compression rates are used, the quantization step largely discards high-frequency noise in which spectral content is represented by a large number of low magnitude coecients. 10 At these very low compression ratios, image degradation is im- perceptible (referred to as``as``visually lossless''; Fig 3a). As noted above, JPEG2000 allows users to specify the maximum change in pixel value permitted. ...
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Citations
... Nevertheless, the utilisation of proprietary compression techniques substantially amplifies the expenses and exertion involved in transmitting data across diverse systems, hence compelling the implementation of digital communication standards [4]. It is important to acknowledge that prior assessments of medical image compression techniques have been documented in published literature [5][6][7][8][9][10][11][12][13][14]. ...
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... Several methods evaluate the clinical acceptance of the compression level [46]. The first is the numerical analysis of the pixel before and after compression [47]. This simple method is recommended for calculating the mean pixel error for the compressed image but has no correlation with radiologists' evaluations and therefore has no clinical significance. ...
Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.
... CT IQA metrics have been researched considering their own characteristics. MSE, PSNR, and SSIM have been used for CT IQA as basic guidelines for the evaluation of algorithms; however, they do not correlate well with human perception and have little relationship with diagnostic utility [22]. Some classical methods for the estimation of CT IQA are the modulation transfer function and the noise power spectrum (NPS) [23,24]. ...
Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) image protocols while keeping the radiation dose as low as reasonably achievable. In the medical domain, IQA is based on how well an image provides a useful and efficient presentation necessary for physicians to make a diagnosis. Moreover, IQA results should be consistent with radiologists’ opinions on image quality, which is accepted as the gold standard for medical IQA. As such, the goals of medical IQA are greatly different from those of natural IQA. In addition, the lack of pristine reference images or radiologists’ opinions in a real-time clinical environment makes IQA challenging. Thus, no-reference IQA (NR-IQA) is more desirable in clinical settings than full-reference IQA (FR-IQA). Leveraging an innovative self-supervised training strategy for object detection models by detecting virtually inserted objects with geometrically simple forms, we propose a novel NR-IQA method, named deep detector IQA (D2IQA), that can automatically calculate the quantitative quality of CT images. Extensive experimental evaluations on clinical and anthropomorphic phantom CT images demonstrate that our D2IQA is capable of robustly computing perceptual image quality as it varies according to relative dose levels. Moreover, when considering the correlation between the evaluation results of IQA metrics and radiologists’ quality scores, our D2IQA is marginally superior to other NR-IQA metrics and even shows performance competitive with FR-IQA metrics.
... 2,3 With the application of image compression and decompression techniques, such expectations and challenges can be met to preserve all clinically relevant information. [4][5][6][7][8] These techniques can be divided into two types: lossless and lossy. Lossless compression methods are the group of algorithms that allow you to recover all data to its initial intact state after the decompression process, therefore these methods are known to result in a lower compression ratio. ...
... In 1992, the Joint Photographic Experts Group developed the JPEG lossy compression algorithm, which has been introduced as an ISO standard and is widely used [7,22]. Despite the relatively smaller size of JPEG file format [23,24], it did not impair in the diagnosis of IRR and/or ERR. Furthermore, one of the main advantages of the use of images with greater compression is the facilitated digital storage and online transmission. ...
Objectives
To evaluate the influence of different image file formats of digital radiographic images on the diagnosis of external (ERR) and internal root resorption (IRR).
Materials and methods
Thirty-four human teeth were selected. For ERR, 20 teeth were used (10 control and 10 with simulated ERR), and for IRR, 14 teeth were used (before and after IRR simulation). Digital periapical radiographs were acquired using the Digora Toto system and exported in four different image file formats: TIFF, BMP, PNG, and JPEG, totaling 192 radiographs. Five examiners evaluated the images using the JPEGView software and scored the detection of ERR or IRR on a 5-point scale. Sensitivity, specificity, accuracy, and the area under the ROC curve were obtained for the diagnosis of ERR and IRR in the different image file formats. Two-way ANOVA compared the diagnostic values between the file formats and the Kappa test assessed intra- and inter-examiner agreement. The significance level was set at 5% (α = 0.05).
Results
The mean values of intra-examiner agreement were substantial (0.740) for ERR and almost perfect (0.836) for IRR and, inter-examiner was fair (0.263) and moderate (0.421), respectively. No statistically significant differences were found among the different file formats for the diagnostic values of ERR and IRR.
Conclusion
The file format of digital radiographs does not influence the diagnosis of ERR and IRR.
Clinical relevance
Digital radiographic images may be susceptible to computational factors; however, they can be stored in multiple file formats without affecting the diagnosis of dental root resorptions.
... Image compression is also an important step in teleradiology and the original image files size are too big for transmission. So compression of these images makes them possible for easy transmission by at the expanse of image quality (Erickson, 2002). ...
Teleradiology is the practice of transmitting the different radiological reports generated via X-rays, CT-scan and MRI from one part of the world to the another or one location to the other to get the consultation and interpretation from the different expert radiologists. In Veterinary, it was first commercially available in the early 90s. Previously it was poorly developed due to the limited internet speed and lack of essential software but currently, the practice of teleradiology in Veterinary Medicine has become universal due to proper broadband connection, availability of image compression, PACS and DICOM software. This review aims to summarize the goals, applications, transmission, method of image acquisition and digitalization, types of teleradiology, its legal issues and market in Veterinary Medicine.
... [22][23][24] The lossy compression method consists of 3 steps-transformation, quantization, and encoding-and eliminates less important information, thereby reducing transmission and storage requirements. 24,25 The theoretical major loss in image information occurs during the quantization step. 25,26 However, the present study revealed no significant influence on the diagnosis of proximal caries lesions between the file formats assessed. ...
... 24,25 The theoretical major loss in image information occurs during the quantization step. 25,26 However, the present study revealed no significant influence on the diagnosis of proximal caries lesions between the file formats assessed. Thus, considering the benefits of storing and sharing files of smaller sizes, the JPEG file format can be used without concern. ...
Objectives
This in-vitro study aimed to evaluate the influence of the radiographic image file format and the transmission application (app) on the diagnosis of proximal caries lesions.
Study Design
Twenty bitewing radiographs of 40 posterior human teeth placed in phantoms were acquired using the Digora Toto digital sensor. All images were exported as TIFF, BMP, PNG, and JPEG and transmitted online via WhatsApp and Messenger. Five examiners evaluated the radiographs with no online transmission and as transmitted through the 2 apps for the presence of proximal caries lesions by using a 5-point scale. The reference standard for caries lesions was established using micro-computed tomography. Two-way ANOVA compared values of sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve (Az) (α=0.05). The kappa test was used to assess intra- and interexaminer agreements.
Results
Sensitivity, specificity, accuracy, and Az values showed no significant differences in the diagnosis of proximal caries lesions between the different image file formats (p ≥ 0.773) and transmission apps (p ≥ 0.608). Intraexaminer agreement was substantial (κ = 0.742) and interexaminer agreement was moderate (κ = 0.475).
Conclusion
The digital file format and transmission app did not influence the radiographic diagnosis of proximal caries lesions.
... Also in the medical imaging field, to cope with the tremendously increasing volume of digital images, selected studies have investigated the effects of lossy compression on the quality of medical pictures and their diagnostic potential (Erickson, 2002;Seeram, 2006;Flint, 2012). Although compression ratios of the raw (Bae & Whiting, 2001) or final images between 1:5 and 1:15 seem adequate to guarantee correct medical diagnosis (Koff et al., 2008;ESR, 2011), the results vary between studies and strongly depend on the imaging modality and used validation metrics. ...
Modern detectors used at synchrotron tomographic microscopy beamlines typically have sensors with more than 4–5 mega-pixels and are capable of acquiring 100–1000 frames per second at full frame. As a consequence, a data rate of a few TB per day can easily be exceeded, reaching peaks of a few tens of TB per day for time-resolved tomographic experiments. This data needs to be post-processed, analysed, stored and possibly transferred, imposing a significant burden onto the IT infrastructure. Compression of tomographic data, as routinely done for diffraction experiments, is therefore highly desirable. This study considers a set of representative datasets and investigates the effect of lossy compression of the original X-ray projections onto the final tomographic reconstructions. It demonstrates that a compression factor of at least three to four times does not generally impact the reconstruction quality. Potentially, compression with this factor could therefore be used in a transparent way to the user community, for instance, prior to data archiving. Higher factors (six to eight times) can be achieved for tomographic volumes with a high signal-to-noise ratio as it is the case for phase-retrieved datasets. Although a relationship between the dataset signal-to-noise ratio and a safe compression factor exists, this is not simple and, even considering additional dataset characteristics such as image entropy and high-frequency content variation, the automatic optimization of the compression factor for each single dataset, beyond the conservative factor of three to four, is not straightforward.
... However, the issue of image quality limits the development of lossy image compression techniques. [1][2][3] Several quantitative and qualitative methods are used to ascertain the performance of image quality for lossy image compression. Distortion assessment approaches, such as mean-squared error (MSE) and peak signal-to-noise ratio (PSNR), are frequently used to evaluate the quality of reconstructed images. ...
Image quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well‐known image compression algorithms. Then, error‐based and correlation‐based metrics were applied to these images. The correlation‐based metrics agree with human‐vision evaluations in experiments, but the error‐based metrics do not. Inspired by the positive result of the correlation‐based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human‐vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation‐based metrics.
... 11 In general, generally modern compression standards are not context-based regarding bi-level segmented medical images and the irreversible techniques are not preferred for medical image compression. 20,21 The characteristics of the bi-level (segmented medical data) differ from natural image data in terms of entropy level, compactness, 22 and the energy of the image matrix, which is defined as an absolute value of enclosed curve or volume, and morphological structure. 23 And thus, the compression techniques that are not specifically designed for medical data cannot completely reveal the corresponding redundancy. ...
Background
DICOM standard does not have modules that provide the possibilities of two‐dimensional Presentation States to three‐dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective.
Purpose
To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context‐based reversible methods.
Material and Methods
A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1‐DUAL and 20 T2‐SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography‐angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well‐established reversible binary compression methods PNG, JPEG‐LS, JPEG‐XR, CCITT‐G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context‐based (3D‐RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric.
Results
Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT‐AAA, 56.7 to 0.3 MB for CT‐liver. Moreover, compared to the performance of well‐established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D‐RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi‐level images, contributes to compression performance. Although the performance of well‐established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D‐RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance.
Conclusion
Adaptive and context‐based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re‐produce visualizations for better teleradiology practices preserving all interaction mechanisms.
