Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers

Wavelet-Based Lossy Compression Techniques For Medical Images

Medical imaging is a powerful and useful tool for radiologists and consultants, allowing them to improve and facilitate their diagnosis. Worldwide, X-ray images represent 60% of the total amount of radiological images, the remaining consists of more newly developed image modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), Positron Emission Tomography (PET), Single Photon Emission Computerized Tomography (SPECT), Nuclear Medicine (NM), and Digital Subtraction Angiography (DSA). Image communication systems for medical images have bandwidth and image size constraints that result in time-consuming transmission of uncompressed raw image data. Thus image compression is a key factor to improve transmission speed and storage, but it risks losing relevant medical information. The radiology standard Digital Imaging and Communications in Medicine (DICOM3) provides rules for compression using lossless Joint Photographic Expert Group (JPEG) methods. However, at the moment there are no rules for acceptance of lossy compression in medical imaging and it is an extremely subjective decision. Acceptable levels of compression should never compromise diagnostic information. Wavelet technology has emerged as a promising compression tool to achieve a high compression ratio while maintaining an acceptable fidelity of image quality

Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography

Computer aided diagnosis of miliary TB in chest X-rays

Includes bibliography.With the improvement in computer technology, Computer Aided Diagnosis (CAD) is becoming an increasingly more powerful tool for radiologists. The focus of this project was on CAD of pulmonary miliary tuberculosis. Several methods for enhancing lung textures were discussed as an aid to the radiologist in diagnosing miliary TB. Some statistical approaches and template matching methods were used to measure characteristics of both healthy and unhealthy (miliary TB) lung textures. These measurements were evaluated to see if a computer can be programmed to differentiate between lung texture from a healthy lung and lung texture from a lung with miliary TB

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Automated evaluation of radiodensities in a digitized mammogram database using local contrast estimation

Mammographic radiodensity is one of the strongest risk factors for developing breast cancer and there exists an urgent need to develop automated methods for predicting this marker. Previous attempts for automatically identifying and quantifying radiodense tissue in digitized mammograms have fallen short of the ideal. Many algorithms require significant heuristic parameters to be evaluated and set for predicting radiodensity. Many others have not demonstrated the efficacy of their techniques with a sufficient large and diverse patient database. This thesis has attempted to address both of these drawbacks in previous work. Novel automated digital image processing algorithms are proposed that have demonstrated the ability to rapidly sift through digitized mammogram databases for accurately estimating radiodensity. A judicious combination of point-processing, statistical, neural and contrast enhancement techniques have been employed for addressing this formidable problem. The algorithms have been developed and exercised using over 700 mammograms obtained from multiple age and ethnic groups and digitized using more than one type of X-ray digitizer. The automated algorithms developed in this thesis have been validated by comparing the estimation results using 40 of these mammograms with those predicted by a previously established manual segmentation technique. The automated algorithms developed in this thesis show considerable promise to be extremely useful in epidemiological studies when correlating other behavioral and genetic risk factors with mammographic radiodensity