Performance Analysis of Region of Interest Based Compression Method for Medical Images

2014 ◽  
Author(s):  
Rushabh Shah ◽  
Priyanka Sharma ◽  
Rutvi Shah
Keyword(s):  
Performance Analysis ◽  
Medical Images ◽  
Region Of Interest ◽  
Compression Method

scholarly journals A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ali Ibrahim Khaleel ◽  
Nik Adilah Hanin Zahri ◽  
Muhammad Imran Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Medical Images ◽  
Region Of Interest ◽  
Input Image ◽  
Compression Method ◽  
Medical Diagnoses ◽  
The Neural Network ◽  
Similar Images

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.

Differential Image Compression for Telemedicine: A Novel Approach

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Adnan Alam Khan ◽  
Dr. Asadullah Shah ◽  
Saghir Muhammad
Keyword(s):  
Image Compression ◽  
Medical Information ◽  
Emerging Technologies ◽  
Medical Images ◽  
Image Transmission ◽  
Medical Doctors ◽  
Medical Sciences ◽  
Compression Method ◽  
Paramedical Staff ◽  
Novel Approach

Telemedicine is one of the most emerging technologies of applied medical sciences. Medical information related to patients is transmitted and stored for references and consultations. Medical images occupy huge space; in order to transmit these images may delay the process of image transmission in critical times. Image compression techniques provide a better solution to combat bandwidth scarcity problems, and transmit same image in a much lower bandwidth requirements, more faster and at the same time maintain quality. In this paper a differential image compression method is developed in which medical images are taken from a wounded patient and are compressed to reduce the bit rate of these images. Results indicate that on average 25% compression on images is achieved with 3.5 MOS taken from medical doctors and other paramedical staff the ultimately user of the images.

Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

2020 ◽  
pp. 2150021
Author(s):  
Urvashi Sharma ◽  
Meenakshi Sood ◽  
Emjee Puthooran
Keyword(s):  
Medical Image ◽  
Region Of Interest ◽  
Block Size ◽  
Threshold Value ◽  
Hospital Performance ◽  
Jpeg 2000 ◽  
Compression Method ◽  
Compression Efficiency ◽  
Important Region

A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.

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