In this paper, we present a novel image representation that renders it possible to access natural scenes by local semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the semantic content of images. The basic idea of the semantic modeling is to classify local image regions into semantic concept classes such as water, rocks, or foliage. Images are represented through the frequency of occurrence of these local concepts. Through extensive experiments, we demonstrate that the image representation is well suited for modeling the semantic content of heterogenous scene categories, and thus for categorization and retrieval.

The image representation also allows us to rank natural scenes according to their semantic similarity relative to certain scene categories. Based on human ranking data, we learn a perceptually plausible distance measure that leads to a high correlation between the human and the automatically obtained typicality ranking. This result is especially valuable for content-based image retrieval where the goal is to present retrieval results in descending semantic similarity from the query.

Semantic Modeling of Natural Scenes for Content-Based Image Retrieval

Histology image analysis for carcinoma detection and grading

Region-based image retrieval using integrated color, shape, and location index

This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the "big three": Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.

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Expert Systems in Clinical Microbiology

Determining similarities among data objects is a core task of content-based multimedia retrieval systems. Approximating data object contents via flexible feature representations, such as feature signatures, multimedia retrieval systems frequently determine similarities among data objects by applying distance functions. In this paper, we compare major state-of-the-art similarity measures applicable to flexible feature signatures with respect to their qualities of effectiveness and efficiency. Furthermore, we study the behavior of the similarity measures by discussing their properties. Our findings can be used in guiding the development of content-based retrieval applications for numerous domains.

A comparative study of similarity measures for content-based multimedia retrieval

This article provides a framework to describe and compare content-based image retrieval systems. Sixteen contemporary systems are described in detail, in terms of the following technical aspects: querying, relevance feedback, result presentation, features, and matching. For a total of 44 systems we list the features that are used. Of these systems, 35 use any kind of color features, 28 use texture, and only 25 use shape features.

Features in Content-based Image Retrieval Systems: a Survey.

Features in Content-based Image Retrieval Systems: a Survey

Short consultations and a large and growing amount of available medical information make searching for suitable information difficult for general practitioners. Thus information is often not searched for or not found, diminishing the quality of care. We propose a system that offers decision support by combining medical information sources with data from the electronic patient record. A first evaluation shows that a system like Medintel can be a useful supportive tool and can increase the quality of care provided by general practitioners.

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Medintel: decision support for general practitioners: a case study.

In diagnostic decision-support systems, a test-selection facility serves to select tests that are expected to yield the largest decrease in the uncertainty about a patient's diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.

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On the Behaviour of Information Measures for Test Selection

In the medical domain, establishing a diagnosis typically amounts to reasoning about the unobservable truth, based upon a set of indirect observations from diagnostic tests. A diagnostic test may not be perfectly reliable, however. To avoid misdiagnosis, therefore, the reliability characteristics of the test should be taken into account upon reasoning. In this paper, we address the issue of modelling such characteristics in a probabilistic network. We argue that the standard reliability characteristics that are generally available from the literature have to be further detailed, for example by experts, before they can be included in a network. We illustrate this and related modelling issues by means of a real-life probabilistic network in oncology.

Danielle Sent

Papers

Features in Content-based Image Retrieval Systems: a Survey.

Features in Content-based Image Retrieval Systems: a Survey

Medintel: decision support for general practitioners: a case study.

On the Behaviour of Information Measures for Test Selection

Detailing Test Characteristics for Probabilistic Networks