Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Chinmay Chakraborty
Keyword(s):  
Chronic Wounds ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Set Partitioning ◽  
Compression Technique ◽  
Telemedicine System ◽  
Diagnostic Quality ◽  
Segmentation Accuracy ◽  
Fuzzy C Means Clustering ◽  
Bandwidth Savings

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

2021 ◽  
pp. 345-364
Author(s):  
Chinmay Chakraborty
Keyword(s):  
Chronic Wounds ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Set Partitioning ◽  
Compression Technique ◽  
Telemedicine System ◽  
Diagnostic Quality ◽  
Segmentation Accuracy ◽  
Fuzzy C Means Clustering ◽  
Bandwidth Savings

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

ROBUST JPEG2000 IMAGE TRANSMISSION THROUGH LOW SNR MULTI-CARRIER CDMA SYSTEM IN FREQUENCY-SELECTIVE RAYLEIGH FADING CHANNELS: A PERFORMANCE STUDY

2011 ◽  
Vol 09 (02) ◽  
pp. 283-303 ◽  
Author(s):  
KATHIYAIAH THIYAGU ◽  
T. H. OH
Keyword(s):  
Fading Channels ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Multipath Fading ◽  
Cdma System ◽  
Performance Study ◽  
Compression Technique ◽  
Multipath Fading Channel ◽  
Low Snr ◽  
Code Division Multiple

The demand for high data rate transmission is ever increasing every day. Multi-carrier code division multiple access (MC-CDMA) system is considered as the forerunner and advancement in the mobile communication system. In this paper, two types of JPEG2000 lossily-compressed test images are transmitted through an MC-CDMA channel in low SNR (as low as 4 dB) environment and their quality are evaluated objectively by using peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The test images are all compressed from ratio of 10 : 1 up to 70 : 1 and the system involves multi-user image transmission in near real-time low SNR (±5 dB). It is found that JPEG2000 image compression technique that applies wavelet transform performed quite well in the low SNR multipath fading channel — as low as 4 dB, and this looks promising for future applications.

An Efficient Lossless Medical Image Compression Using Hybrid Algorithm

2014 ◽  
Vol 984-985 ◽  
pp. 1276-1281
Author(s):  
C. Priya ◽  
T. Kesavamurthy ◽  
M. Uma Priya
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Set Partitioning ◽  
Compression Technique ◽  
Computational Speed ◽  
Reconstruction Quality ◽  
Spiht Algorithm ◽  
Pyramid Structures

Recently many new algorithms for image compression based on wavelets have been developed.This paper gives a detailed explanation of SPIHT algorithm with the combination of Lempel Ziv Welch compression technique for image compression by MATLAB implementation. Set partitioning in Hierarchical trees (SPIHT) is one of the most efficient algorithm known today. Pyramid structures have been created by the SPIHT algorithm based on a wavelet decomposition of an image. Lempel Ziv Welch is a universal lossless data compression algorithm guarantees that the original information can be exactly reproduced from the compressed data.The proposed methods have better compression ratio, computational speed and good reconstruction quality of the image. To analysis the proposed lossless methods here, calculate the performance metrics as Compression ratio, Mean square error, Peak signal to Noise ratio. Key Words-LempelZivWelch (LZW),SPIHT,Wavelet

scholarly journals Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Giganti ◽  
Alex Kirkham ◽  
Veeru Kasivisvanathan ◽  
Marianthi-Vasiliki Papoutsaki ◽  
Shonit Punwani ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Diagnostic Quality ◽  
Prostate Mri ◽  
Prostate Imaging ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted ◽  
Contrast Enhanced

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

scholarly journals Image multiplexing using residue number system coding over MIMO-OFDM communication system

2019 ◽  
Vol 9 (6) ◽  
pp. 4815
Author(s):  
Mohamed Ibrahim Youssif ◽  
Amr ElSayed Emam ◽  
Mohamed Abd ElGhany
Keyword(s):  
Image Quality ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Number System ◽  
Residue Number System ◽  
Signal To Noise ◽  
Residue Number ◽  
Mimo Ofdm ◽  
Noise Ratio

<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>

scholarly journals Improving Image Transmission by Using Polar Codes and Successive Cancellation List Decoding

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Álvaro Garcia ◽  
Maria De Lourdes Melo Guedes Alcoforado ◽  
Francisco Madeiro ◽  
Valdemar Cardoso Da Rocha Jr.
Keyword(s):  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Structural Similarity ◽  
Image Transmission ◽  
Polar Codes ◽  
Code Construction ◽  
Successive Cancellation ◽  
Natural Choice ◽  
Shift Keying

This paper investigates the transmission of grey scale images encoded with polar codes and de-coded with successive cancellation list (SCL) decoders in the presence of additive white Gaussian noise. Po-lar codes seem a natural choice for this application be-cause of their error-correction efficiency combined with fast decoding. Computer simulations are carried out for evaluating the influence of different code block lengths in the quality of the decoded images. At the encoder a default polar code construction is used in combination with binary phase shift keying modulation. The results are compared with those obtained by using the clas-sic successive cancellation (SC) decoding introduced by Arikan. The quality of the reconstructed images is assessed by using peak signal to noise ratio (PSNR) and the structural similarity (SSIM) index. Curves of PSNR and SSIM versus code block length are presented il-lustrating the improvement in performance of SCL in comparison with SC.

Learning Aided Digital Image Compression Technique for Medical Application

2016 ◽  
pp. 400-422
Author(s):  
Kandarpa Kumar Sarma
Keyword(s):  
Image Compression ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Medical Application ◽  
Learning Systems ◽  
Signal To Noise ◽  
Compression Technique ◽  
Compressed Images ◽  
Digital Image Compression ◽  
Efficiency And Reliability

The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.

scholarly journals Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5345
Author(s):  
Ahmad K. Aijazi ◽  
Laurent Malaterre ◽  
Laurent Trassoudaine ◽  
Thierry Chateau ◽  
Paul Checchin
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Consensus Algorithm ◽  
3D Point Cloud ◽  
Automated Mapping ◽  
Segmentation Accuracy ◽  
Underground Infrastructure ◽  
Fuzzy C Means Clustering ◽  
3D Scans ◽  
Planning And Construction

Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than 7 % , an overall segmentation accuracy of 85 % and an overall modeling error of less than 5 % . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.

scholarly journals INTELLIGENT DETECTION AND CLASSIFICATION OF MICROCALCIFICATION IN COMPRESSED MAMMOGRAM IMAGE

2015 ◽  
Vol 34 (3) ◽  
pp. 183 ◽  
Author(s):  
Benjamin Joseph ◽  
Baskaran Ramachandran ◽  
Priyadharshini Muthukrishnan
Keyword(s):  
Probabilistic Neural Network ◽  
Signal To Noise Ratio ◽  
Model Performance ◽  
High Sensitivity ◽  
Processing Technique ◽  
Set Partitioning ◽  
Experimental Result ◽  
Channel Noise ◽  
Bit Rate ◽  
Mammogram Image

The main contribution of this article is introducing an intelligent classifier to distinguish between benign and malignant areas of micro-calcification in companded mammogram image which is not proved or addressed elsewhere. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The prime contribution of this work is that details of ROI and Non-ROI regions extracted using multi-wavelet transform are coded at variable bit rate using proposed Region Based Set Partitioning in Hierarchical Trees (RBSPIHT) before storing or transmitting the image. Image reconstructed during retrieval or at the receiving end is preprocessed to remove the channel noise and to enhance the diagnostic contrast information. Then the preprocessed image is classified as normal or abnormal (benign or malignant) using Probabilistic neural network. Segmentation of cancerous region is done using Fuzzy C-means Clustering (FCC) algorithm and the cancerous area is computed. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38% at an average compression rate and Peak Signal to Noise Ratio (PSNR) of 0.5bpp and 58dB respectively.

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