Christina Pavlopoulou

Residual test coverage monitoring

Structural coverage criteria are often used as an indicator of the thoroughness of testing, but complete satisfaction of a criterion is seldom achieved. When a software product is released with less than 100% coverage, testers are explicitly or implicitly assuming that executions satisfying the remaining test obligations (the residue) are either infeasible or occur so rarely that they have negligible impact on quality. Violation of this assumption indicates shortcomings in the testing process. Monitoring in the deployed environment, even in the beta test phase, is typically limited to error and sanity checks. Monitoring the residue of test coverage in actual use can provide additional useful information, but it is unlikely to be accepted by users unless its performance impact is very small. Experience with a prototype tool for residual test coverage monitoring of Java programs suggests that, at least for statement coverage, the simple strategy of removing all probes except those corresponding to the residue of coverage testing reduces execution overhead to acceptably low levels.

From Polyvariant flow information to intersection and union types

Many polyvariant program analyses have been studied in the 1990s, including k-CFA, polymorphic splitting, and the cartesian product algorithm. The idea of polyvariance is to analyze functions more than once and thereby obtain better precision for each call site. In this paper we present an equivalence theorem which relates a co-inductively-defined family of polyvariant flow analyses and a standard type system. The proof embodies a way of understanding polyvariant flow information in terms of union and intersection types, and, conversely, a way of understanding union and intersection types in terms of polyvariant flow information. We use the theorem as basis for a new flow-type system in the spirit of the λCIL-calculus of Wells, Dimock, Muller and Turbak, in which types are annotated with flow information. A flow-type system is useful as an interface between a flow-analysis algorithm and a program optimizer. Derived systematically via our equivalence theorem, our flow-type system should be a good interface to the family of polyvariant analyses that we study.

Content-based image retrieval from large medical databases

The main goal of content based image retrieval is to efficiently retrieve images that are visually similar to a query image. In this paper we focus on content based image retrieval from large medical databases, outline the problems specific to this area, and describe the recent advances in the field. We also present some of the more significant results obtained with ASSERT (Automatic Search and Selection Engine with Retrieval Tools), the content based image retrieval system developed in our laboratory.

From polyvariant flow information to intersection and union types

Many polyvariant program analyses have been studied in the 199Os, including k-CFA, poly-k-CFA, and the Cartesian product algorithm. The idea of polyvariance is to analyze functions more than once and thereby obtain better precision for each call site. In this paper we present the first formal relationship between polyvariant analysis and standard notions of type. In the spirit of Nielson and Nielson, we study a parameterized flow analysis which can be instantiated to the analyses of Agesen, Schmidt, and as a simple case also 0-CFA. Extended with safety checks, the flow analysis accepts and rejects programs, much like a type checker. We prove that if a program can be safety-checked by a finitary instantiation of the flow analysis, then it can also be typed in a type system with intersection types, union types, subtyping, and recursive types, but no universal or existential quantifiers. This provides a framework for designing and understanding combinations of flow analyses and type systems.

CBIR for medical images - an evaluation trial

Content-based image retrieval (CBIR) has the potential to provide medical doctors with a powerful resource to help make accurate diagnoses. To aid in diagnosis, a CBIR system must retrieve similar images from the same (unknown) disease class as the patient. We have implemented a CBIR system that first predicts the disease class of the query image and then retrieves the n images nearest to the query image from the pool of images with the predicted disease class. With the cooperation of residents/radiologists at Indiana University Medical Center and the Department of Radiology at the University of Wisconsin we have recently completed an evaluation of our system. The results show that when using our system, the diagnostic accuracy of the group increased on average by 32% over diagnosis without any reference materials.

Content-based image retrieval for medical imagery

The main goal of content based image retrieval is to efficiently retrieve images that are visually similar to a query image. In this paper we will focus on content based image retrieval from large medical databases, outline the problems specific to this area, and describe the recent advances in the field. We will also present some of the more significant results obtained with ASSERT (Automatic Search and Selection Engine with Retrieval Tools), the content based image retrieval system developed in our laboratory.

A Web-based CBIR-assisted learning tool for radiology education-anytime and anyplace

We propose a Web-based learning tool for assisting and enhancing radiology education. Central to the learning tool is our content-based image retrieval (CBIR) system for medical image databases. In contrast to the learning resources available through traditional radiology curricula, our learning tool represents a more flexible, instantly updateable, and a richer learning environment that can be used anytime and anyplace. Experts from diverse disciplines-radiology, computer vision, and education-had to pool their knowledge and resources together to bring about the development of this learning tool.

An interactive framework for boundary delineation for medical CBIR

CBIR systems designed for medical applications often require that a human in the loop demarcate the pathology bearing regions in the image, since fully automatic extraction of such regions is still not possible. In our CBIR system for the domain of HRCT images of the lung, physicians do not find this interaction too onerous since the boundary surrounding a pathology bearing region does not have to be precise. But in our new domain, a liver image database, that is unfortunately not the case. The boundaries supplied by the physician must correspond precisely to the outline of the liver or to the boundary of a pathology bearing region inside the liver. To meet this demand, we have developed a new user interaction framework for semi-automatic boundary extraction. All that a physician has to do is to click on a couple of pixels on the boundary to be extracted. The system then tries its best to extend the boundary as far as possible, sometimes even extracting the entire contour correctly. When errors occur, all that the physician is called upon to do is to click on where a correction to the boundary needs to take place. In this manner, an entire boundary can be specified with very little input from the human, which is a most important consideration with physicians as he/she can hardly be expected to click on every boundary point.

Globally Optimal Interactive Boundary Extraction Using Markov Chain Modeling

We present a novel boundary-based (discontinuity tracking) hierarchical statistical criterion to address the interactive contour extraction problem. Our criterion relies on a Markov Chain representation of the boundary and can be efficiently optimized using Dijkstra’s algorithm for solving the shortest paths problem. Unlike other criteria optimized with Dijkstra’s algorithm, ours is capable of extracting geometrically complex boundaries even when the features incorporated in the objective function are based only on user markings on a small part of the image. The critical quantity in our criterion that yields the above-mentioned results is a normalization factor that boosts the probability of a particular boundary segment based on the candidate boundary segments in its vicinity. Although similar in spirit to the technique of non-maximum suppression routinely employed in edge detection, our method boosts gradually the probability of a particular segment given its surroundings using windows of increasing size in a hierarchical fashion.