Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.

The aim of this study was to determine the visually lossless threshold of a wavelet-based compression algorithm in case of microcalcification cluster detection in mammography. The threshold was determined by means of observer performance using a set of digitized mammograms. In addition, the transfer characteristics of the compression algorithm were assessed by means of image-quality parameters using computer-generated test images. The observer performance study was based on rating performed by four independent radiologists, who reviewed 68 mammograms, from the Digital Database for Screening Mammography (DDSM), at six different compression ratios. Receiver operating characteristics (ROC) analysis was performed on observers' responses and the area under ROC curve (A(z)) was calculated at each compression ratio for each observer. The parameters used for assessment of transfer characteristics of the compression algorithm were input/output response, noise, high-contrast response, and low-contrast-detail response. The computer-generated test image, used for this assessment, mimicked mammographic image characteristics (pixel size, pixel depth, and noise) as well as microcalcification characteristics (size and contrast). The ROC analysis for microcalcification cluster detection indicated a threshold at compression ratio 40:1, as Student's t-test shows statistically significant differences in A(z) values (p<0.05) for compression ratios 70:1 and 100:1. Observers' grading of mammogram quality lowers this threshold at 25:1. Low-contrast-detail detectability in the transfer characteristics study indicate a threshold of 35:1, whereas non-perceptibility of image-quality-parameters degradation lowers this threshold to 30:1. The ROC and transfer characteristics analysis provided comparable thresholds, indicating the potential use of the latter in limiting the target range of compression ratios for subsequent observer studies.

Compression assessment based on medical image quality concepts using computer-generated test images.

Compression algorithms are widely used in medical imaging systems for efficient image storage, transmission, and display. In the acceptance of lossy compression algorithms in the clinical environment, important factors are the assessment of 'visually lossless' compression thresholds, as well as the development of assessment methods requiring fewer data and time than observer performance based studies. In this study a set of quantitative measurements related to medical image quality parameters is proposed for compression assessment. Measurements were carried out using region of interest (ROI) operations on computer-generated test images, with characteristics similar to radiographic images. As a paradigm, the assessment of the lossy Joint Photographic Expert Group (JPEG) algorithm, available in a telematics application for healthcare, is presented. A compression ratio of 15 was found as the visually lossless threshold for the JPEG lossy algorithm, in agreement with previous observer performance studies. Up to this ratio low contrast discrimination is not affected, image noise level is decreased, high contrast line-pair amplitude is decreased by less than 3%, and input/output gray level differences are minor (less than 1%). This type of assessment provides information regarding the type of loss, offering cost and time benefits, in parallel with the advantages of test image adaptation to the requirements of a certain imaging modality and clinical study.

A protocol-based evaluation of medical image digitizers.

Medical film digitizers play an important transitory role as digital-to-analogue bridges in radiology. Their use requires performance evaluation to assure medical image quality. A complete quality control protocol is presented, based on a set of test objects adaptable to the specification of various digitizers. The protocol includes parameters such as uniformity, input-output response, noise, geometric distortion, spatial resolution, low contrast discrimination, film slippage and light leakage, as well as associated measurement methods. The applicability of the protocol is demonstrated with two types of medical film digitizers; a charge-coupled device (CCD) digitizer and a laser digitizer. The potential value of the protocol is also discussed.

A tool for designing digital test objects for module performance evaluation in medical digital imaging.

Currently, medical digital imaging systems are characterized by the introduction of additional modules such as digital display, image compression and image processing, as well as film printing and digitization. These additional modules require performance evaluation to ensure high image quality. A tool for designing computer-generated test objects applicable to performance evaluation of these modules is presented. The test objects can be directly used as digital images in the case of film printing, display, compression and image processing, or indirectly as images on film in the case of digitization. The performance evaluation approach is quality control protocol based. Digital test object design is user-driven according to specifications related to the requirements of the modules being tested. The available quality control parameters include input/output response curve, high contrast resolution, low contrast discrimination, noise, geometric distortion and field uniformity. The tool has been designed and implemented according to an object oriented approach in Visual C++ 5.0, and its user interface is based on the Microsoft Foundation Class Library version 4.2, which provides interface items such as windows, dialog boxes, lists, buttons, etc. The compatibility with DICOM 3.0 part 10 image formats specifications allows the integration of the tool in the existing software framework for medical digital imaging systems. The capability of the tool is demonstrated by direct use of the test objects in case of image processing, and indirect use of the test objects in case of film digitization.