scholarly journals Biomedical Image Compression Techniques for Clinical Image Processing

2020 ◽  
Vol 16 (12) ◽  
pp. 133
Author(s):  
Abdul Khader Jilani Saudagar
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Image Compression ◽  
Image Coding ◽  
Medical Images ◽  
Focal Point ◽  
Clinical Image ◽  
Coding System ◽  
Quality Factors ◽  
Clinical Images

Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

Study of the Discrete Wavelet Transform Based on JPEG2000 Image Compression Algorithm

2013 ◽  
Vol 464 ◽  
pp. 411-415
Author(s):  
Jin Cai ◽  
Shuo Wang
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Image Coding ◽  
Dynamic Range ◽  
Compression Algorithm ◽  
Discrete Wavelet ◽  
Coding System ◽  
Jpeg 2000 ◽  
Image Compression Algorithm

JPEG 2000 is a new image coding system that uses state-of-the-art compression techniques based on wavelet technology. As interactive multimedia technologies evolve, the requirements for the file format used to store the image data continue to evolve. The size and bit depth collected for an image to increase the resolution and extend the dynamic range and color gamut. Discrete Wavelet transform based embedded image coding method is the basis of JPEG2000. Image compression algorithm for the proper use and display of the image is a requirement for digital photography.

scholarly journals Image Compression Using Neural Networks: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 135-153
Author(s):  
Haval Tariq Sadeeq ◽  
Thamer Hassan Hameed ◽  
Abdo Sulaiman Abdi ◽  
Ayman Nashwan Abdulfatah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Image Coding ◽  
Image Data ◽  
Coding System ◽  
Memory Space ◽  
Coding Efficiency ◽  
Huge Data ◽  
The Neural Network

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

Image Compression Based on Wavelet Transform

2014 ◽  
Vol 568-570 ◽  
pp. 749-752
Author(s):  
Chang Li Yan ◽  
Quan Cai Deng ◽  
Xin Zhang
Keyword(s):  
Image Processing ◽  
Wavelet Analysis ◽  
Image Compression ◽  
Image Coding ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Wavelet Theory ◽  
Two Dimensional Image ◽  
Ezw Algorithm ◽  
Selection Of

The wavelet analysis has some important applications in image processing, including image compression, image de-noising and so on.Wavelet analysis for two-dimensional image compression is a key aspect in the field of its applications.This paper studied the application of wavelet analysis in BMP image coding, the characteristics of wavelet coefficients and wavelet subimage, these lay the foundation for further selection of wavelet coefficients and optimize, and analyzed the theory of EZW algorithm, illustrates the better results of the applications on using wavelet theory in image processing.

Research and Exploration on Static Image Compression Technology Based on JPEG2000

2014 ◽  
Vol 644-650 ◽  
pp. 4182-4186
Author(s):  
Hua Tian ◽  
Ming Jun Li ◽  
Huan Huan Liu
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Processing System ◽  
Coding System ◽  
Compression Process ◽  
Static Image ◽  
Compression Time ◽  
Speedup Ratio ◽  
Universal Applicability ◽  
Static Form

This article introduces GPU-accelerated image processing parallel computing technology into standard core coding system of JPEG2000 static image compression and accelerates and designs the image compression process using CUDA acceleration principle. It also establishes the algorithm of image pixel array layered and reconstruction coding and realizes the coding of this algorithm using VC software. In order to verify the effectiveness and universal applicability of the algorithm and procedures, this paper compresses four images of different sizes and pixels in the static form of the JPEG2000. Through the comparison of the compression time, we can find that GPU hardware image processing system has a higher speedup ratio. With the increase of pixel and size, speedup ratio gradually increased which means that GPU acceleration has good adaptability.

scholarly journals A Critical Analysis of Biomedical Image Classification on Deep Learning

2021 ◽  
pp. 113-123
Author(s):  
Rose Lu ◽  
Dawei Pan
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Background Information ◽  
Clinical Image ◽  
Coding System ◽  
Clinical Images ◽  
Biomedical Image ◽  
Second Phases ◽  
High Level ◽  
Diagnostic Technologies

In computer-aided diagnostic technologies, deep convolutional neural image compression classifications are a crucial method. Conventional methods rely primarily on form, colouring, or feature descriptors, and also their configurations, the majority of which would be problem-specific that has been depicted to be supplementary in image data, resulting in a framework that cannot symbolize high problem entities and has poor prototype generalization capability. Emerging Deep Learning (DL) techniques have made it possible to build an end-to-end model, which could potentially general the last detection framework from the raw clinical image dataset. DL methods, on the other hand, suffer from the high computing constraints and costs in analytical modelling and streams owing to the increased mode of accuracy of clinical images and minimal sizes of data. To effectively mitigate these concerns, we provide a techniques and paradigm for DL that blends high-level characteristics generated from a deep network with some classical features in this research. The following stages are involved in constructing the suggested model: Firstly, we supervisedly train a DL model as a coding system, and as a consequence, it could convert raw pixels of medical images into feature extraction, which possibly reflect high-level ideologies for image categorization. Secondly, using image data background information, we derive a collection of conventional characteristics. Lastly, to combine the multiple feature groups produced during the first and second phases, we develop an appropriate method based on deep neural networks. Reference medical imaging datasets are used to assess the suggested method. We get total categorization reliability of 90.1 percent and 90.2 percent, which is greater than existing effective approaches.

Real Time Image Fusion Based Technique for Medical Images

2020 ◽  
Vol 17 (9) ◽  
pp. 4500-4508
Author(s):  
H. R. Ramya ◽  
B. K. Sujatha
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Image Fusion ◽  
Real Time ◽  
Medical Images ◽  
Computation Time ◽  
Real Time Image ◽  
Fuzzy Logic Technique ◽  
Time Image

To tackle the cost of storage and storage space with fast-growing technologies, the image fusion is playing an important role in several image-processing areas such as medical-imaging and satelliteimaging. This fused picture is appropriate for machine perception, human visual analysis or further analysis assignment. Recently the computing method such as fuzzy logic model has been extensively used in the field of image-processing due to the uniqueness of handling uncertain modeling. The fuzzy logic based image-fusion model generally performed better with respect to other existing image fusion models. In this paper, we considered type-2 fuzzy logic, which has similar function to earlier fuzzy logic technique but consist more functionality that allows optimized management of higher degrees under uncertainty. Interval type-2 fuzzy-logic-system (IT2FLS) are widely used fuzzy sets due to their ease of use and computational simplicity. A real time image fusion (RTIF) technique that is based on the IT2FLS is used to overcome the excess computation time and nonlinear uncertainties, which is present in the medical images. In the result simulation section, we have shown that our proposed model has taken less computation time and provided better quality assessment matrices with respect to existing system.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

2020 ◽  
Author(s):  
Xiaoyu He ◽  
Juan Su ◽  
Guangyu Wang ◽  
Kang Zhang ◽  
Navarini Alexander ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Image Classification ◽  
Bullous Pemphigoid ◽  
Skin Diseases ◽  
Pemphigus Vulgaris ◽  
Classification Model ◽  
Clinical Image ◽  
Clinical Images ◽  
Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

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