We present a novel algorithm for the registration of multiple temporally related point sets. Although our algorithm is derived in a general setting, our primary motivating application is coronary tree matching in multi-phase cardiac spiral CT. Our algorithm builds upon the fast, outlier-resistant Coherent Point Drift (CPD) algorithm, but incorporates temporal consistency constraints between the point sets, resulting in spatiotemporally smooth displacement fields. We preserve the speed and robustness of the CPD algorithm by using the technique of separable surrogates within an EM (Expectation-Maximization) optimization framework, while still minimizing a global registration cost function employing both spatial and temporal regularization. We demonstrate the superiority of our novel temporally consistent group-wise CPD algorithm over a straightforward pair-wise approach employing the original CPD algorithm, using coronary trees derived from both simulated and real cardiac CT data. In all the tested configurations and datasets, our method presents lower average error between tree landmarks compared to the pairwise method. In the worst case, the difference is around few micrometers but in the better case, our method divides by two the error from the pairwise method. This improvement is especially important for a dataset with numerous outliers. With a fixed set of parameter that has been tuned automatically, our algorithm yields better results than the original CPD algorithm which shows the capacity to register without a priori information on an unknown dataset.
We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of
discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can
speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude
through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology
workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive
compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a
semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical
characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity,
such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose
visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination
between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear
classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation
error rate of 1.4%. We further show that the proposed method can prune ≈90% of the area of pathological slides while
maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.
Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone
process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate
between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel
textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in
breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose
malignancy. In conjunction with the maximum relevance - minimum redundancy feature selection technique, we
obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification
FROC and AFROC analyses are useful in medical imaging to characterize detection performance for the case of
multiple lesions. We had previously developed1 ideal FROC and AFROC observers. Their performance is ideal in
that they maximize the area or any partial area under the FROC or AFROC curve. Such observers could be useful
in imaging system optimization or in assessing human observer efficiency. However, the performance evaluation
of these ideal observers is impractically computationally complex. We propose 3 reasonable assumptions under
which the ideal observers reduce approximately to a particular form of a scan-statistic observer. Performance
for the "scan-statistic-reduced ideal observer" can be evaluated far more rapidly albeit with slight error than
that of the originally proposed ideal observer. Through simulations, we confirm the accuracy of our approximate
ideal observers. We also compare the performance of our approximate ideal observer with that of a conventional
scan-statistic observer and show that the performance of our approximate ideal observer is significantly greater.
Detection of multiple lesions (signals) in images is a medically important task and Free-response Receiver Operating Characteristic (FROC) analyses and its variants, such as Alternative FROC (AFROC) analyses, are commonly used to quantify performance in such tasks. However, ideal observers that optimize FROC or AFROC performance metrics have not yet been formulated in the general case. If available, such ideal observers may turn out to be valuable for imaging system optimization and in the design of computer aided diagnosis (CAD) techniques for lesion detection in medical images. In this paper we derive ideal AFROC and FROC observers. They are ideal in that they maximize, amongst all decision strategies, the area under the associated AFROC or FROC curve. In addition these ideal observers minimize Bayes risk for particular choices of cost constraints. Calculation of observer performance for these ideal observers is computationally quite complex. We can reduce this complexity by considering forms of these observers that use false positive reports derived from signal-absent images only. We present a performance comparison of our ideal AFROC observer versus that of a more conventional scan-statistic observer.
For the 2-class detection problem (signal absent/present), the likelihood ratio is an ideal observer in that it minimizes Bayes risk for arbitrary costs and it maximizes AUC, the area under the ROC curve. The AUC-optimizing property makes it a valuable tool in imaging system optimization. If one considered a different task, namely, joint detection and localization of the signal, then it would be similarly valuable to have a decision strategy that optimized a relevant scalar figure of merit. We are interested in quantifying performance on decision tasks involving location uncertainty using the LROC methodology. We derive decision strategies that maximize the area under the LROC curve, ALROC. We show that these decision strategies minimize Bayes risk under certain reasonable cost constraints. We model the detection-localization task as a decision problem in three increasingly realistic ways. In the first two models, we treat location as a discrete parameter having finitely many values resulting in an (L+1) class classification problem. In our first simple model, we do not include search tolerance effects and in the second, more general, model, we do. In the third and most general model, we treat location as a continuous parameter and also include search tolerance effects. In all cases, the essential proof that the observer maximizes ALROC is obtained with a modified version of the Neyman-Pearson lemma using Lagrange multiplier methods. A separate form of proof is used to show that in all three cases, the decision strategy minimizes the Bayes risk under certain reasonable cost constraints.
Detection and localization performance with signal location uncertainty may be summarized by Figures of Merit (FOM's) obtained from the LROC curve. We consider model observers that may be used to compute the two LROC FOM's: ALROC and PCL, for emission tomographic MAP reconstruction. We address the case background-known-exactly (BKE) and signal known except for location. Model observers may be used, for instance, to rapidly prototype studies that use human observers. Our FOM calculation is an ensemble method (no samples of reconstructions needed) that makes use of theoretical expressions for the mean and covariance of the reconstruction. An affine local observer computes a response at each location, and the maximum of these is used as the global observer - the response needed by the LROC curve. In previous work, we had assumed the local observers to be independent and normally distributed, which allowed the use of closed form expressions to compute the FOM's. Here, we relax the independence assumption and make the approximation that the local observer responses are jointly normal. We demonstrate a fast theoretical method to compute the mean and covariance of this joint distribution (for the signal absent and present cases) given the theoretical expressions for the reconstruction mean and covariance. We can then generate samples from this joint distribution and rapidly (since no reconstructions need be computed) compute the LROC FOM's. We validate the results of the procedure by comparison to FOM's obtained using a gold-standard Monte Carlo method employing a large set of reconstructed noise trials.