Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms.

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16 : 1, 20 : 1, 40 : 1, 60.4 : 1, 80: 1, and 106 : 1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16: 1 (T = -0.7780; p = 0.4370), 20 : 1 (T = 1.0361; p = 0.3007), and 40 : 1 (T = 1.6966; p = 0.0904); and statistically significant for uncompressed versus 60.4 : 1 (T = 5.8883; p < 0.008), 80 : 1 (T = 7.8414; p < 0.008), and 106 : 1 (T = 17.5034; p = < 0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60 : 1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40 : 1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20 : 1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics.

Free-response receiver operating characteristic evaluation of lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms.

PURPOSE: To assess the effects of two irreversible wavelet-based compression algorithms--Joint Photographic Experts Group (JPEG) 2000 and object-based set partitioning in hierarchical trees (SPIHT)--on the detection of clusters of microcalcifications and masses on digitized mammograms. MATERIALS AND METHODS: The use of the images in this retrospective image-collection study was approved by the institutional review board, and patient informed consent was not required. One hundred twelve mammographic images (28 with one or two clusters of microcalcifications, 19 with one mass, 17 with both abnormal findings, and 48 with normal findings) obtained in 60 women who ranged in age from 25 to 79 years were digitized and compressed at 40:1 and 80:1 by using the JPEG2000 and object-based SPIHT methods. Five experienced radiologists were asked to locate and rate clusters of microcalcifications and masses on the original and compressed images in a free-response receiver operating characteristic (FROC) data acquisition paradigm. Observer performance was evaluated with the jackknife FROC method. RESULTS: The mean FROC figures of merit for detecting clusters of microcalcifications, masses, and both radiographic findings on uncompressed images were 0.80, 0.81, and 0.72, respectively. With object-based SPIHT 80:1 compression, the corresponding values were larger than the values for uncompressed images by 0.005, 0.009, and -0.005, respectively. The 95% confidence interval for the differences in figures of merit between compressed and uncompressed images was -0.039, 0.033 for the microcalcification finding; -0.055, 0.034 for the mass finding; and -0.039, 0.030 for both findings. Because each of these confidence intervals includes zero, no significant difference in detection accuracy between uncompressed and object-based SPIHT 80:1 compression was observed at a P value of 5%. The F test of the null hypothesis that all of the modes (uncompressed and four compressed modes) were equivalent yielded the following results: F = 0.255, P = .903 for the microcalcification finding; F = 0.340, P = .848 for the mass finding; and F = 0.122, P = .975 for both findings. CONCLUSION: To within the accuracy of these measurements, lossy compression of digital mammographic data at 80:1 with JPEG2000 or the object-based SPIHT algorithm can be performed without decreasing the rate of detection of clusters of microcalcifications and masses.

ROC evaluation of statistical wavelet-based analysis of brain activation in [15O]-H2O PET scans.

This paper presents and evaluates a wavelet-based statistical analysis of PET images for the detection of brain activation areas. Brain regions showing significant activations were obtained by performing Student's t tests in the wavelet domain, reconstructing the final image from only those wavelet coefficients that passed the statistical test at a given significance level, and discarding artifacts introduced during the reconstruction process. Using Receiver Operating Characteristic (ROC) curves, we have compared this statistical analysis in the wavelet domain to the conventional image-domain Statistical Parametric Mapping (SPM) method. For obtaining an accurate assessment of sensitivity and specificity, we have simulated realistic single subject [15O]-H2O PET studies with different hyperactivation levels of the thalamic region. The results obtained from an ROC analysis show that the wavelet approach outperforms conventional SPM in identifying brain activation patterns. Using the wavelet method, activation areas detected were closer in size and shape to the region actually activated in the reference image.

Wavelet-based ultra-high compression of multidimensional NMR data sets.

The application of a lossy data compression algorithm based on wavelet transform to 2D NMR spectra is presented. We show that this algorithm affords rapid and extreme compression ratios (e.g., 800:1), providing high quality reconstructed 2D spectra. The algorithm was evaluated to ensure that qualitative and quantitative information are retained in the compressed NMR spectra. Whilst the maximum compression ratio that can be achieved depends on the number of signals and on the difference between the most and the least intense peaks (dynamic range), a compression ratio of 80:1 is affordable even for the challenging case of homonuclear 2D experiments of large biomolecules.

Region-based wavelet coding methods for digital mammography.

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.