Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images

The need for transmission and archive of mammograms and ultrasound Images has dramatically increased in tele-healthcare applications. Such images require large amount of' storage space which affect transmission speed. Therefore an effective compression scheme is essential. Compression of these images. in general. laces a great challenge to compromise between the higher compression ratio and the relevant diagnostic information. Out of the many studied compression schemes. lossless . IPl. (i- LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are chosen based on a comprehensive experimental study carried on a large number of mammograms and ultrasound images of different sizes and texture. The lossless schemes are evaluated based on the compression ratio and compression speed. The distortion in the image quality which is introduced by lossy methods evaluated based on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio (PSNR). It is found that lossless compression can achieve a modest compression ratio 2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than lossless ones but at the price of the image quality which may impede diagnostic conclusions. In this work, a new compression approach called Ilvbrid Region-based Image Compression Scheme (IIYRICS) has been proposed for the mammograms and ultrasound images to achieve higher compression ratios without compromising the diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied on the remaining non region of interest. The results clearly show that this hybrid strategy can yield high compression ratios with perfect reconstruction of diagnostic relevant regions, achieving high speed transmission and less storage requirement. For the sample images considered in our experiment, the compression ratio increases approximately ten times. However, this increase depends upon the size of the region of interest chosen. It is also föund that the pre-processing (contrast stretching) of region of interest improves compression ratios on mammograms but not on ultrasound images

The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

The impact of skull bone intensity on the quality of compressed CT neuro images

International audienceThe increasing use of technologies such as CT and MRI, along with a continuing improvement in their resolution, has contributed to the explosive growth of digital image data being generated. Medical communities around the world have recognized the need for efficient storage, transmission and display of medical images. For example, the Canadian Association of Radiologists (CAR) has recommended compression ratios for various modalities and anatomical regions to be employed by lossy JPEG and JPEG2000 compression in order to preserve diagnostic quality. Here we investigate the effects of the sharp skull edges present in CT neuro images on JPEG and JPEG2000 lossy compression. We conjecture that this atypical effect is caused by the sharp edges between the skull bone and the background regions as well as between the skull bone and the interior regions. These strong edges create large wavelet coefficients that consume an unnecessarily large number of bits in JPEG2000 compression because of its bitplane coding scheme, and thus result in reduced quality at the interior region, which contains most diagnostic information in the image. To validate the conjecture, we investigate a segmentation based compression algorithm based on simple thresholding and morphological operators. As expected, quality is improved in terms of PSNR as well as the structural similarity (SSIM) image quality measure, and its multiscale (MS-SSIM) and informationweighted (IW-SSIM) versions. This study not only supports our conjecture, but also provides a solution to improve the performance of JPEG and JPEG2000 compression for specific types of CT images

Assessment of diagnostic usefulness of vibration of the common rail system in the diesel engine

The paper presents an assessment of diagnostic usefulness of the vibration measurements and analysis of selected components of the common rail system in the diesel engine. Measurements were performed of the selected components of the CR system and changes were determined in the time and frequency structure of the recorded vibration signals. The research has shown that vibration recorded on the injector's housing and the rail of the common rail system is useful diagnostic information for the evaluation of the technical condition of the fuel supply system of a compression ignition engine

Influence of study design on digital pathology image quality evaluation : the need to define a clinical task

Despite the current rapid advance in technologies for whole slide imaging, there is still no scientific consensus on the recommended methodology for image quality assessment of digital pathology slides. For medical images in general, it has been recommended to assess image quality in terms of doctors’ success rates in performing a specific clinical task while using the images (clinical image quality, cIQ). However, digital pathology is a new modality, and already identifying the appropriate task is difficult. In an alternative common approach, humans are asked to do a simpler task such as rating overall image quality (perceived image quality, pIQ), but that involves the risk of nonclinically relevant findings due to an unknown relationship between the pIQ and cIQ. In this study, we explored three different experimental protocols: (1) conducting a clinical task (detecting inclusion bodies), (2) rating image similarity and preference, and (3) rating the overall image quality. Additionally, within protocol 1, overall quality ratings were also collected (task-aware pIQ). The experiments were done by diagnostic veterinary pathologists in the context of evaluating the quality of hematoxylin and eosin-stained digital pathology slides of animal tissue samples under several common image alterations: additive noise, blurring, change in gamma, change in color saturation, and JPG compression. While the size of our experiments was small and prevents drawing strong conclusions, the results suggest the need to define a clinical task. Importantly, the pIQ data collected under protocols 2 and 3 did not always rank the image alterations the same as their cIQ from protocol 1, warning against using conventional pIQ to predict cIQ. At the same time, there was a correlation between the cIQ and task-aware pIQ ratings from protocol 1, suggesting that the clinical experiment context (set by specifying the clinical task) may affect human visual attention and bring focus to their criteria of image quality. Further research is needed to assess whether and for which purposes (e.g., preclinical testing) task-aware pIQ ratings could substitute cIQ for a given clinical task

Accuracy of transcranial magnetic stimulation and a Bayesian latent class model for diagnosis of spinal cord dysfunction in horses

Background: Spinal cord dysfunction/compression and ataxia are common in horses. Presumptive diagnosis is most commonly based on neurological examination and cervical radiography, but the interest into the diagnostic value of transcranial magnetic stimulation (TMS) with recording of magnetic motor evoked potentials has increased. The problem for the evaluation of diagnostic tests for spinal cord dysfunction is the absence of a gold standard in the living animal. Objectives: To compare diagnostic accuracy of TMS, cervical radiography, and neurological examination. Animals: One hundred seventy-four horses admitted at the clinic for neurological examination. Methods: Retrospective comparison of neurological examination, cervical radiography, and different TMS criteria, using Bayesian latent class modeling to account for the absence of a gold standard. Results: The Bayesian estimate of the prevalence (95% CI) of spinal cord dysfunction was 58.1 (48.3%-68.3%). Sensitivity and specificity of neurological examination were 97.6 (91.4%-99.9%) and 74.7 (61.0%-96.3%), for radiography they were 43.0 (32.3%-54.6%) and 77.3 (67.1%-86.1%), respectively. Transcranial magnetic stimulation reached a sensitivity and specificity of 87.5 (68.2%-99.2%) and 97.4 (90.4%-99.9%). For TMS, the highest accuracy was obtained using the minimum latency time for the pelvic limbs (Youden's index = 0.85). In all evaluated models, cervical radiography performed poorest. Clinical Relevance: Transcranial magnetic stimulation-magnetic motor evoked potential (TMS-MMEP) was the best test to diagnose spinal cord disease, the neurological examination was the second best, but the accuracy of cervical radiography was low. Selecting animals based on neurological examination (highest sensitivity) and confirming disease by TMS-MMEP (highest specificity) would currently be the optimal diagnostic strategy

New Watermarking Scheme for Security and Transmission of Medical Images for PocketNeuro Project

We describe a new Watermarking system of medical information security and terminal mobile phone adaptation for PocketNeuro project. The later term refers to a Project created for the service of neurological diseases. It consists of transmitting information about patients \"Desk of Patients\" to a doctor\'s mobile phone when he is visiting or examining his patient. This system is capable of embedding medical information inside diagnostic images for security purposes. Our system applies JPEG Compression to Watermarked images to adapt them to the doctor\'s mobile phone. Experiments performed on a database of 30-256x256 pixel-sized neuronal images show that our Watermarking scheme for image security is robust against JPEG Compression. For the purpose of increasing the image Watermarking robustness against attacks for an image transmission and to perform a large data payload, we encode with Turbo-Code image-embedded bits information. Fidelity is improved by incorporation of the Relative Peak Signal-to-Noise Ratio (RPSNR) as a perceptual metric to measure image degradation

Mechanical chest-compression devices: current and future roles

Purpose of review: It is recognised that the quality of CPR is an important predictor of outcome from cardiac arrest yet studies consistently demonstrate that the quality of CPR performed in real life is frequently sub-optimal. Mechanical chest compression devices provide an alternative to manual CPR. This review will consider the evidence and current indications for the use of these devices. Recent findings: Physiological and animal data suggest that mechanical chest compression devices are more effective than manual CPR. However there is no high quality evidence showing improved outcomes in humans. There are specific circumstances where it may not be possible to perform manual CPR effectively e.g. during ambulance transport to hospital, en-route to and during cardiac catheterisation, prior to organ donation and during diagnostic imaging where using these devices may be advantageous. Summary: There is insufficient evidence to recommend the routine use of mechanical chest compression devices. There may be specific circumstances when CPR is difficult or impossible where mechanical devices may play an important role in maintaining circulation. There is an urgent need for definitive clinical and cost effectiveness trials to confirm or refute the place of mechanical chest compression devices during resuscitation