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Free-Response Receiver Operating Characteristic Evaluation of Lossy JPEG2000 and Object-based Set Partitioning in Hierarchical Trees Compression of Digitized Mammograms¹

¹ From the Laboratorio de Imagen Médica, Hospital General Universitario Gregorio Marañón, C/Ibiza 43, 28009 Madrid, Spain (M.P.); the Laboratorio de Investigación Imagen Radiológica, Departamento de Radiología (M.S., J.M.C., J.J.V.), and Departamento de Electrónica y Computacion (P.G.T.), Universidade de Santiago de Compostela, Santiago de Compostela, Spain; the Complexo Hospitalario de Santiago de Compostela, Santiago de Compostela, Spain (M.S., J.M.C., J.V., G.P., C.S., J.J.V.); the Dept of Radiology, Univ of Iowa Hospitals and Clinics, Iowa City, Ia (K.S.B., L.L.F.); and the Dept of Radiology, Univ of Pittsburgh, Pittsburgh, Pa (D.P.C.). Received Jun 4, 2004; revision requested Aug 10; revision received Nov 4; accepted Dec 14. Address correspondence to M.P. (e-mail: monica{at}mce.hggm.es).

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.

© RSNA, 2005

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