MICROANGIOGRAM VIDEO COMPRESSION USING ADAPTIVE PREDICTION

Coronary angiography is an X-ray examination of the heart\u27s arteries. This is an essential technique for diagnosis of heart damages. Image sequences from digital angiography contain areas of high diagnostic interest. Loss of information due to compression for regions of interest (ROI) in angiograms is not tolerable. Since Commercially available technology such as JPEG and MPEG do not satisfy medical requirements due to their severe blockartifacts. In this paper, a new compression algorithm that achieves high compression ratio and excellent reconstruction quality for video rate or sub-video rate angiograms is developed. The proposed algorithm exploits temporal spatial and spectral redundancies in backward adaptive fashion with Extremely low side information. An experimental result shows that the proposed scheme provides significant improvements in compression efficiencies

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors

Implementation of Transform Based Techniques in Digital Image Watermarking

Digital image watermarking is used to resolve the problems of data security and copyright protection. In many applications of digital watermarking, watermarked image of good quality are required. But here is a trade-off between number of embedded watermark images and quality of watermarked images. This aspect is quite important in case of multiple digital image watermarking. This project presents a robust digital image watermarking using discrete cosine transform (DCT) method. Compression on a watermarked image can significantly affect the detection of the embedded watermark. The detection of the presence or absence of a watermarked in an image is often affected if the watermarked image has undergone compression. Compression can also be considered as an attack on watermarked images. To show that a particular watermarking scheme is robust against compression, simulation is often relied DOI: 10.17762/ijritcc2321-8169.15084

Diagnostically lossless coding of X-ray angiography images based on background suppression

X-ray angiography images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the large amount of images generated daily, coding of X-ray angiography images is becoming essential. This paper proposes a diagnostically lossless coding method based on automatic segmentation of the focal area using ray-casting and α-shapes. The diagnostically relevant Region of Interest is first identified by exploiting the inherent symmetrical features of the image. The background is then suppressed and the resulting images are encoded using lossless and progressive lossy-to-lossless methods, including JPEG-LS, JPEG2000, H.264 and HEVC. Experiments on a large set of X-ray angiography images suggest that our method correctly identifies the Region of Interest. When compared to the case of coding with no background suppression, the method achieves average bit-stream reductions of nearly 34% and improvements on the reconstruction quality of up to 20 dB-SNR for progressive decoding

Estratégias de arquivo de imagiologia cardíaca

Mestrado em Engenharia BiomédicaA produção de imagens médicas em formato digital tem vindo a crescer nas mais variadas instituições prestadoras de cuidados de saúde, representando neste momento, um importante e imprescindível elemento de suporte à decisão médica. Por outro lado, impõe novos desafios ao nível do armazenamento, gestão e transmissão dessas mesmas imagens. Os meios complementares de diagnóstico imagiológicos geram, normalmente, um elevado volume de dados. A compressão revela-se como a única ferramenta que permite simultaneamente aumentar o tempo de armazenamento dos procedimentos e diminuir o seu tempo de transmissão. Este trabalho consistiu no desenvolvimento de uma estratégia alternativa de compressão sem perdas e visualmente sem perdas de imagens médicas provenientes de uma unidade clínica dotada com as mais recentes inovações tecnológicas no campo da imagiologia cardíaca. As novas estratégias alternativas de compressão foram aplicadas em imagens de Angiografia Coronária e Tomografia Computorizada cardíaca, de modo a explorar a redundância (no tempo ou no espaço respectivamente) entre imagens consecutivas que caracterizam estas modalidades de imagem. Estas estratégias são baseadas em métodos alternativos de pré-processamento das tramas em memória e em codificadores de carácter genérico open source. Nesta dissertaçãoo pretende-se também apresentar o estado da arte no que respeita às técnicas de compressão de imagem cardiovascular nomeadamente as adoptadas pela norma DICOM e comparar os seus desempenhos com os do método alternativo proposto nesta tese. A estratégia alternativa permitiu, como iremos ver, obter taxas de compressão significativas ao nível dos melhores codificadores actualmente dispon íveis e identificar a codificação visually lossless como método de maior interesse para a imagiologia cardíaca dinâmica.The acquisition of digital medical images has been growing in the most diverse health care institutions, representing at the moment, an important and indispensable element of medical decision support. On the other hand, it imposes new challenges in the storage, management and transmission of these images. Medical imaging modalities usually produce high volumes of data. Image compression becomes the only tool that can simultaneously decrease the medical procedures storage space and transmission time. This work focused the development of an alternative lossless and visually lossless compression strategy of medical images from a health care institution equipped with the latest technological innovations in the field of cardiac imaging. The new compression alternative strategies were implemented in Coronary Angiography and Computerized Tomography cardiac images in order to explore the redundancy (in time and space respectively) between consecutive images that characterize this type of image. These strategies are based on alternative methods of pre-processing the frames in memory and on generic open source encoders. In this thesis we also present the state-of-the-art techniques of cardiovascular image compression particularly those adopted by the standard DICOM and compare their performance with those of the alternative method here proposed. As we shall see, the alternative strategy achieved significant compression rates at the level of the best available encoders and allowed us to identify visually lossless compression as the one of greatest interest for dinamic cardiac imagiology

Compression of 4D medical image and spatial segmentation using deformable models

Ph.DDOCTOR OF PHILOSOPH

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images

The need for transmission and archive of mammograms and ultrasound Images has dramatically increased in tele-healthcare applications. Such images require large amount of' storage space which affect transmission speed. Therefore an effective compression scheme is essential. Compression of these images. in general. laces a great challenge to compromise between the higher compression ratio and the relevant diagnostic information. Out of the many studied compression schemes. lossless . IPl. (i- LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are chosen based on a comprehensive experimental study carried on a large number of mammograms and ultrasound images of different sizes and texture. The lossless schemes are evaluated based on the compression ratio and compression speed. The distortion in the image quality which is introduced by lossy methods evaluated based on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio (PSNR). It is found that lossless compression can achieve a modest compression ratio 2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than lossless ones but at the price of the image quality which may impede diagnostic conclusions. In this work, a new compression approach called Ilvbrid Region-based Image Compression Scheme (IIYRICS) has been proposed for the mammograms and ultrasound images to achieve higher compression ratios without compromising the diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied on the remaining non region of interest. The results clearly show that this hybrid strategy can yield high compression ratios with perfect reconstruction of diagnostic relevant regions, achieving high speed transmission and less storage requirement. For the sample images considered in our experiment, the compression ratio increases approximately ten times. However, this increase depends upon the size of the region of interest chosen. It is also föund that the pre-processing (contrast stretching) of region of interest improves compression ratios on mammograms but not on ultrasound images