MEDICAL IMAGES COMPRESSION BASED ON SPIHT AND BAT INSPIRED ALGORITHMS

There is a significant necessity to compress the medical images for the purposes of communication and storage.Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. Inmedical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it ispreferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this paper, a hybridtechnique of Set Partition in Hierarchical Tree (SPIHT) and Bat inspired algorithms have been utilized for Lossless compressionthe interest region, and the non-interest region is loosely compressed with the Discrete Cosine Transform (DCT) technique.The experimental results present that the proposed hybrid technique enhances the compression performance and ratio. Also,the utilization of DCT increases compression performance with low computational complexity

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

Selective Medical Image Compression using Wavelet Techniques

Selective Image Compression (SeLIC) is a compression technique where explicitly defined regions of interest (Roi) are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner. Such techniques are of great interest in telemedicine or medical imaging applications with large storage requirements. In this paper we introduce and compare different techniques based on wavelet transforms and demonstrate their good performance which is mainly due to the spatial locality of the wavelet transform domain

Medical Image Compression based on ROI using Integer Wavelet Transform

Medical imaging refers to techniques and processes used to create images of various parts of the human body for diagnostic and treatment purposes within digital health. With the increased use of digital images in clinical settings, it has become necessary to use various compression methods, both lossless and lossy, in order to reduce their cost of storage or transmission. While lossy compression alternatives allow high compression rates, there are legal limitations that such images including MRI, ultrasound, X-Ray and CT-Scan should be stored in a format without loss of information. This work proposes a digital image compression mechanism compatible with the Digital Imaging and Communications in Medicine (DICOM) standard that takes advantage of the IDWT capabilities to preserve the diagnostic quality of the regions of interest, through lossless encoding, while the rest of the image, composed of zones less relevant, is compressed with for JPEG compression. The results, in terms of Compression Ratio, MSE and PSNR are found to be quite satisfactory both quantitatively and qualitatively

Selective Compression of Medical Images via Intelligent Segmentation and 3D-SPIHT Coding

ABSTRACT SELECTIVE COMPRESSION OF MEDICAL IMAGES VIA INTELLIGENT SEGMENTATION AND 3D-SPIHT CODING by Bohan Fan The University of Wisconsin-Milwaukee, 2018 Under the Supervision of Professor Zeyun Yu With increasingly high resolutions of 3D volumetric medical images being widely used in clinical patient treatments, efficient image compression techniques have become in great demand due to the cost in storage and time for transmission. While various algorithms are available, the conflicts between high compression rate and the downgraded quality of the images can partially be harmonized by using the region of interest (ROI) coding technique. Instead of compressing the entire image, we can segment the image by critical diagnosis zone (the ROI zone) and background zone, and apply lossless compression or low compression rate to the former and high compression rate to the latter, without losing much clinically important information. In this thesis, we explore a medical image transmitting process that utilizes a deep learning network, called 3D-Unet to segment the region of interest area of volumetric images and 3D-SPIHT algorithm to encode the images for compression, which can be potentially used in medical data sharing scenario. In our experiments, we train a 3D-Unet on a dataset of spine images with their label ground truth, and use the trained model to extract the vertebral bodies of testing data. The segmented vertebral regions are dilated to generate the region of interest, which are subject to the 3D-SPIHT algorithm with low compress ratio while the rest of the image (background) is coded with high compress ratio to achieve an excellent balance of image quality in region of interest and high compression ratio elsewhere

Three-dimensional adaptive image compression concept for medical imaging : application to computed tomography angiography for peripheral arteries

Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information

Multi-regional Adaptive Image Compression (AIC) for hip fractures in pelvis radiography

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content