Ringo W. K. Lam

Extraction of semantic features of histological images for content-based retrieval of images

This paper presents an approach for automatically assign histologically meaningful labels to tissue slide images. This approach is implemented as part of a larger system, I- Browse, which combines iconic and semantic content for intelligent image browsing. Our approach partitioned an input image into a number of subimages. A set of texture features based on Gabor filterings and color histogram which capture the visual characteristics of each of the subimages were computed. These image feature measurements then form the input to a pattern classifier which gives an initial coarse label assignment to subimages based on a hierarchical clustering of these image features. To facilitate supervised training of the classifier, a knowledge elicitation tool was developed which allows a histopathologist to assign histological terms to a sample of sub-images obtained from digitized tissue imags. The initial labels and their spatial distribution were then analyzed by a semantic analyzer with the help of a knowledge base which contains prior knowledge of the expected visual appearance of histological images of an organ. The label assigned to the subimages were successive refined through a process of relevant feedback.

A multi-window approach to classify histological features

Medical images are usually composed of different kinds of texture components which are always so much varied that a conventional single window approach cannot capture enough salient information for comparison. This paper applies the widely used multi-channel Gabor filters to demonstrate how a multi-window approach can improve the classification accuracy rate of histological labels. In addition, a most confident window method is proposed to further increase the accuracy rate of the multi-window approach.

Similarity measures for histological image retrieval

A gastro-intestinal (GI) tract histological image is usually composed of texture components with different dimensions and properties. To analyze a histological image, we divide it into an array of sub-images. A feature vector comprising a set of Gabor filters and the intensity statistics is computed in order to classify each sub-image to one of 63 histological labels. To retrieve an image from the database, we compare three similarity measures, shape, neighbour and sub-image frequency distribution. It is found that both neighbour and sub-image frequency distribution similarity measures perform similarly well but the shape similarity measure yields the worst result when retrieving images of different GI tract organs. In general, the sub-image frequency distribution measure is the best choice because it requires less time to compute than the neighbour measure.

An Iconic and Semantic Content-Based Retrieval System for Histological Images

This paper describes an intelligent image retrieval system based on iconic and semantic content of histological images. The system first divides an image into a set of subimages. Then the iconic features are derived from primitive features of color histogram, texture and second order statistics of the subimages. These features are then passed to a high level semantic reasoning engine, which generates hypotheses and requests a number of specific fine feature detectors for verification. After iterating a certain number of cycles, a final histological label map is decided for the submitted image. The system may then retrieve images based on either iconic or semantic content. Annotation is also generated for each image processed.

An object-oriented framework for content-based image retrieval based on 5-tier architecture

Reports a generic object-oriented framework for content-based image retrieval (CBIR) systems. It is designed so that the basic data structures and functionality of a typical CBIR system are provided without sacrificing speed and flexibility. The framework is based on a five-tier architecture that allows modules in different tiers to be developed independently, and thus flexibility is ensured. We show that our framework is able to adapt to a wide range of CBIR applications by applying the framework to the development of two on-going projects: a trademark image retrieval system and a medical (histological) image retrieval system. These applications are briefly discussed.

An intelligent system for integrating semantic and iconic features for image retrieval

The I-Browse project aimed to develop a prototype system to provide facilities for supporting intelligent retrieval of medical images through a combination of iconic and semantic content. The resulting prototype system, I-Browse, is able to extract and represent relevant iconic and semantic information from input images and to automatically generate textual annotations for images. Techniques were also developed for retrieving relevant images from the database given either an example image query or a textual query. The facilities provided by I-Browse were evaluated by medical colleagues and judged to have the potential of alleviating some of the time-consuming tasks that doctors now have to perform daily and providing a means of identifying previously unknown relationships between visual appearance and histological events.

A software framework for combining iconic and semantic content for retrieval of histological images

Content-based Image Retrieval (CBIR) is becoming an important component of a database system as it allows retrieval of images by objective measures such as color and texture. Nevertheless, retrieval of images intelligently by computer is still not common. In addition, different users might have different requirements so we need to address their needs by providing a more flexible retrieval mechanism. Finally, we might want to add CBIR functionality to existing system but none of the existing techniques is able to do this easily because they usually rely on one single environment. In this paper, we describe the design of a histological image retrieval system (I-Browse) that addresses the above three issues.