Similarity searching in medical image databases

We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of “labeled” or “expected” objects (e.g., “heart”, “lungs”, etc.) are common in all images of a given application domain in addition to a variable number of “unexpected” or “unlabeled” objects (e.g., “tumor”, “hematoma”, etc.). The method can answer queries by example, such as “find all X-rays that are similar to Smith's X-ray”. The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate, so that all images up to a prespecified degree of similarity (tolerance) are retrieved. (b) It has no “false dismissals” (i.e., all images qualifying query selection criteria are retrieved). (c) It is much faster than sequential scanning for searching in the main memory and on the disk (i.e., by up to an order of magnitude), thus scaling-up well for large databases     

Similar-shape retrieval in shape data management

Addresses the problem of similar-shape retrieval, where shapes or images in a shape database that satisfy specified shape-similarity constraints with respect to the query shape or image must be retrieved from the database. In its simplest form, the similar-shape retrieval problem can be stated as, “retrieve or select all shapes or images that are visually similar to the query shape or the query image's shape”. We focus on databases of 2D shapes-or equivalently, databases of images of flat or almost flat objects. (We use the terms “object” and “shape” interchangeably). Two common types of 2D objects are rigid objects, which have a single rigid component called a link, and articulated objects, which have two or more rigid components joined by movable (rotating or sliding) joints. An ideal similar-shape retrieval technique must be general enough to handle images of articulated as well as rigid objects. It must be flexible enough to handle simple query images, which have isolated shapes, and complex query images, which have partially visible, overlapping or touching objects. We discuss the central issues in similar-shape retrieval and explain how these issues are resolved in a shape retrieval scheme called FIBSSR (Feature Index-Based Similar-Shape Retrieval). This new similar-shape retrieval system effectively models real-world applications     

Shapeme histogram projection and matching for partial object recognition

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.     

基于3D直方图的彩色图象分割方法

以阈值分割技术为基础，提出了基于3D直方图的生长法和尺度空间聚类方法。分别利用3D空间中同一类目标像素频度的连通性的尺度空间聚类原理，简单而有效地解决了多维阈值分割带来的过度分割问题。该方法对不同类型图象具有很好的适应性，并已成功应用于医学彩色图象处理领域。     

Application of the Cascade-Correlation network to the classification of circularly scanned images

This paper describes an application of the Cascade-Correlation (CC) network to pattern recognition. The pattern recognition task was to simulate an automatic vision inspection system that had to properly classify five different objects. The feature vectors were extracted from 2D images of circularly scanned images and used as inputs for a neural network that was then trained to classify an unknown presented object. The results show that the CC network is viable tool in pattern recognition tasks. It is able to classify partially occluded objects with high accuracy, and to considerably improve classification of noisy images based on simple histogram trimming preprocessing.     

Linear object classes and image synthesis from a single exampleimage

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, simpler techniques are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other “prototypical” objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by the authors (1992). For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively “rotate” high-resolution face images from a single 2D view     

Symmetry as a continuous feature

Symmetry is treated as a continuous feature and a continuous measure of distance from symmetry in shapes is defined. The symmetry distance (SD) of a shape is defined to be the minimum mean squared distance required to move points of the original shape in order to obtain a symmetrical shape. This general definition of a symmetry measure enables a comparison of the “amount” of symmetry of different shapes and the “amount” of different symmetries of a single shape. This measure is applicable to any type of symmetry in any dimension. The symmetry distance gives rise to a method of reconstructing symmetry of occluded shapes. The authors extend the method to deal with symmetries of noisy and fuzzy data. Finally, the authors consider grayscale images as 3D shapes, and use the symmetry distance to find the orientation of symmetric objects from their images, and to find locally symmetric regions in images     

Discovering structural association of semistructured data

Many semistructured objects are similarly, though not identically structured. We study the problem of discovering “typical” substructures of a collection of semistructured objects. The discovered structures can serve the following purposes: 1) the “table-of-contents” for gaining general information of a source, 2) a road map for browsing and querying information sources, 3) a basis for clustering documents, 4) partial schemas for providing standard database access methods, and 5) user/customer interests and browsing patterns. The discovery task is affected by structural features of semistructured data in a nontrivial way and traditional data mining frameworks are inapplicable. We define this discovery problem and propose a solution