When a patient is examined at different times using different protocols, how can we know whether the observed differences in the area or volume estimate are due to the patient, the protocol, or both? Specifically, we ask what is the smallest difference in lung volume that can be computed reliably when two sets of CT data are acquired by varying the number and thickness of the slices, but while holding constant the in-plane resolution. The accuracy and precision of the total lung volume estimates are calculated based on the principles of stereology using uniform design sampling. Comparisons of the lung volume estimate based on fewer slices using stereological principles are employed. A formal test made of the hypothesis that the use of fewer slices can yield satisfactory precision of the lung estimate. It is known that estimation of lung volume based on CT images is sensitive to the acquisition parameters used during scanning: dose, scan time, number of cross-sectional slices, and slice collimation. Those parameters are very different depending on the lung examination required: routine studies or high-resolution detailed studies. Thus, if different protocols are to be used confidently for volume estimation, it is important to understand the factors that influence volume estimate accuracy and to provide the associated confidence intervals for the measurements.
Estimation of ratio or volume of tissue types in an image requires both mensuration and classification. The former is achieved through stereology - a set of techniques that estimate such parameters as length, area, surface area, volume, number and ratio. Classification is achieved by extracting features that capture the discriminating information about tissue type. Typically, manual stereological methods are based on uniform sampling. Nonuniform sampling, however, can yield better results for the same manual effort (hence, more-efficient) if we have prior knowledge of the spatial distribution or location of the parameter of interest.
Both stereology and classification can be performed either manually or by computer. Manual techniques for the combination are based on coarse point counting (low resolution), but assumed perfect pixel classification. Computer-based methods, on the other hand, rely on very fine point counting but in general suffer from imperfect pixel classification. This paper examines the interaction between manual (nonuniform-sampling) and uniform-sampling image processing-based approaches; in particular, we present a measure that combines the classification and measurement errors. Analysis of the variance is used to define the conditions under which each method and its sampling design is and is not advantageous despite its underlying error. This allows the user to choose a method that optimizes overall performance, given the human and machine capabilities available.
Illustrations are given of cases in which each method can be preferable, as measured by the variance of the estimate of the performance that was inferred from the measurement.
Estimation of volume or area of tissue types in an image requires both mensuration and classification. The former is achieved through stereology -- a set of techniques that estimate such parameters as area, volume, surface area, length, and number. Classification is achieved by extracting features that capture the discriminating information about tissue type. Both stereology and classification can be performed either manually or by computer. Manual techniques for the combination are based on coarse point counting (low resolution), but assumed perfect pixel classification. Computer-based methods, on the other hand, rely on very fine point counting but in general suffer from imperfect pixel classification. This paper examines the interaction between manual and image processing-based approaches; in particular, we present a measure that combines the classification and measurement errors. Estimation of the variance is used to define the conditions under which each method is and is not advantageous despite its underlying error. This allows the user to choose a method that optimizes overall performance, given the human and machine capabilities available. Illustrations are given of cases in which each method can be preferable, as measured by the variance of the estimate of the performance that was inferred from the measurement.
Facial disguises of FBI Most Wanted criminals are inevitable and anticipated in our design of automatic/aided target recognition (ATR) imaging systems. For example, man's facial hairs may hide his mouth and chin but not necessarily the nose and eyes. Sunglasses will cover the eyes but not the nose, mouth, and chins. This fact motivates us to build sets of the independent component analyses bases separately for each facial region of the entire alleged criminal group. Then, given an alleged criminal face, collective votes are obtained from all facial regions in terms of 'yes, no, abstain' and are tallied for a potential alarm. Moreover, and innocent outside shall fall below the alarm threshold and is allowed to pass the checkpoint. Such a PD versus FAR called ROC curve is obtained.
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