Delivering Diagnostic Quality Video over Mobile Wireless Networks for Telemedicine

In real-time remote diagnosis of emergency medical events, mobility can be enabled by wireless video communications. However, clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video. Because the medical domain has its own fidelity criteria, it is important to incorporate diagnostic video quality criteria into any video compression system design. To this end, we used flexible algorithms for region-of-interest (ROI) video compression and obtained feedback from medical experts to develop criteria for diagnostically lossless (DL) quality. The design of the system occurred in three steps-measurement of bit rate at which DL quality is achieved through evaluation of videos by medical experts, incorporation of that information into a flexible video encoder through the notion of encoder states, and an encoder state update option based on a built-in quality criterion. Medical experts then evaluated our system for the diagnostic quality of the video, allowing us to verify that it is possible to realize DL quality in the ROI at practical communication data transfer rates, enabling mobile medical assessment over bit-rate limited wireless channels. This work lays the scientific foundation for additional validation through prototyped technology, field testing, and clinical trials

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio

Design of Multiplier for Medical Image Compression Using Urdhava Tiryakbhyam Sutra

Compressing the medical images is one of the challenging areas in healthcare industry which calls for an effective design of the compression algorithms. The conventional compression algorithms used on medical images doesn’t offer enhanced computational capabilities with respect to faster processing speed and is more dependent on hardware resources. The present paper has identified the potential usage of Vedic mathematics in the form of Urdhava Tiryakbhyam sutra, which can be used for designing an efficient multiplier that can be used for enhancing the capabilities of the existing processor to generate enhance compression experience. The design of the proposed system is discussed with respect to 5 significant algorithms and the outcome of the proposed study was testified with heterogeneous samples of medical image to find that proposed system offers approximately 57% of the reduction in size without any significant loss of data

Flexible Macroblock Ordering for Context-Aware Ultrasound Video Transmission over Mobile WiMAX

The most recent network technologies are enabling a variety of new applications, thanks to the provision of increased bandwidth and better management of Quality of Service. Nevertheless, telemedical services involving multimedia data are still lagging behind, due to the concern of the end users, that is, clinicians and also patients, about the low quality provided. Indeed, emerging network technologies should be appropriately exploited by designing the transmission strategy focusing on quality provision for end users. Stemming from this principle, we propose here a context-aware transmission strategy for medical video transmission over WiMAX systems. Context, in terms of regions of interest (ROI) in a specific session, is taken into account for the identification of multiple regions of interest, and compression/transmission strategies are tailored to such context information. We present a methodology based on H.264 medical video compression and Flexible Macroblock Ordering (FMO) for ROI identification. Two different unequal error protection methodologies, providing higher protection to the most diagnostically relevant data, are presented

LOSSLESS AND LOSSY IMAGE COMPRESSION BASED ON DATA FOLDING

Image compression plays a very important role in image processing especially when we have to send the image on the internet. Since imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rates but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in the region of interest, i.e., in diagnostically important regions called region of interest. In the proposed work images are compressed using Data folding technique which uses the property of adjacent neighbour redundancy for prediction. In this method first column folding is applied followed by the row folding iteratively till the image size reduces to predefined value, then arithmetic encoding is applied which results the compressed image at the end before transmitting the data. In this paper lossless compression is achieved only at the region of interest and it is mainly suitable for medical images