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J Gaviria

Publications and source records attributed to J Gaviria.

5 recordsLinked to original sources
PubMed1999

Evaluating the performance of detection algorithms in digital mammography.

The initial and relative evaluation of computer methodologies developed for assisting diagnosis in mammography is usually done by comparing the computer output to ground truth data provided by experts and/or biopsy. Reported studies, however, give little information on how the performance indices of computer assisted diagnosis (CAD) algorithms are determined in this initial stage of evaluation. Several strategies exist in the estimation of the true positive (TP) and false positive (FP) rates with respect to ground truth. Adopting one strategy over another yields different performance rates that can be over- or underestimates of the true performance. Furthermore, the estimation of pairs of TP and FP rates gives a partial picture of the performance of an algorithm. It is shown in this work that new performance indices are needed to fully describe the degree of detection (part or whole) and the type of detection (single calcification, cluster of calcifications, mass, or artifact). Several evaluation strategies were tested. The one that yielded the most realistic performances included the following criteria: The detected area should be at least 50% of the true area and no more than four times the true area in order to be considered TP. At least three true calcifications should be detected to within 1 cm2 with nearest neighbor distances of less than square root(2) cm for a cluster to be considered TP. Separate detection measures should be established and used for artifacts and naturally occurring structures to maximize the benefits of the evaluation. Finally, it is critical that CAD investigators provide information on the tested image set as well as the criteria used for the evaluation of the algorithms to allow comparisons and better understanding of their methodologies.

Algorithms↗
PubMed1998

MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation.

An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.

Adult↗
PubMed1998

Fragmentary window filtering for multiscale lung nodule detection: preliminary study.

RATIONALE AND OBJECTIVES: The authors evaluated computer-assisted diagnostic (CAD) methods used to detect suspicious areas on lung radiographs. MATERIALS AND METHODS: The authors designed a fragmentary window filtering (FWF) algorithm for detecting lung nodule patterns, which generally appear as circular areas of high opacity on the chest radiograph. The FWF algorithm helps differentiate circular patterns from overlapping radiographic background. A multiscale analysis was performed to locate multiscale nodules. Receiver operating characteristic analysis was performed by using a lung nodule that was extracted from a chest radiograph. The nodule underwent scalings and subsequent superimposition onto 140 normal regions of interest from six chest radiographs. RESULTS: The FWF method was superior to the matched filtering method in the detection of suspicious areas. CONCLUSION: The proposed FWF-based method should provide improved detection of lung nodules on chest radiographs.

Algorithms↗