Relevance feedback (RF) has become an active research area in Content-based Image Retrieval (CBIR). RF
attempts to bridge the gap between low-level image features and high-level human visual perception by analyzing
and employing user feedback in an effort to refine the retrieval results to better reflect individual user preference.
Need for overcoming this gap is more evident in medical image retrieval due to commonly found characteristics
in medical images, viz., (1) images belonging to different pathological categories exhibit subtle differences, and
(2) the subjective nature of images often elicits different opinions, even among experts. The National Library
of Medicine maintains a collection of digitized spine X-rays from the second National Health and Nutrition
Examination Survey (NHANES II). A pathology found frequently in these images is the Anterior Osteophyte
(AO), which is of interest to researchers in bone morphometry and osteoarthritis. Since this pathology is
manifested as deviation in shape, we have proposed the use of partial shape matching (PSM) methods for
pathology-specific spinal X-ray image retrieval. Shape matching tends to suffer from the variability in the
pathology expressed by the vertebral shape. This paper describes a novel weight-updating approach to RF. The
algorithm was tested and evaluated on a subset of data selected from the image collection. The ground truth
was established using Macnab's classification to determine pathology type and a grading system developed by
us to express the pathology severity. Experimental results show nearly 20% overall improvement on retrieving
the correct pathological category, from 69% without feedback to 88.75% with feedback.
Surface area and volume measurements provide important information for agriculture and food-processing applications. A machine vision system that uses a nondestructive method to measure volume and surface area of objects with irregular shapes is presented in this paper. The system first takes a series of silhouettes of the object from different directions by rotating the object at a fixed angular interval. The boundary points of each image are then extracted to construct a silhouette. A three-dimensional wire-frame model of the object can be reconstructed by integrating silhouettes obtained from different view angles. Surface area and volume can then be measured by means of surface fitting and approximation on the wire-frame model. System calibration and surface approximation were two major challenges for the design of this machine vision system. A unique centerline calibration method is introduced in this paper. Surface approximation and calculation are also discussed. Examples of applications in agriculture and food processing using this vision system for surface area measurement are included, and its accuracy is verified.
A spine x-ray image retrieval system has been developed for retrieving images based on the pathology information such as the osteophyte. Osteophyte shows only in particular regions on the vertebra. This means the contour information on the vertebra regions that are not of interest hinder the image retrieval relevance precision. Curve matching or partial shape matching (PSM) methods based on dynamic programming for matching shapes with variable number of points and with different data point distribution have been developed to detect the osteophyte with similar shapes. Based on the shape property of spines, corner guided dynamic programming (DP) is introduced as the new enhanced searching strategy which dramatically increases the processing efficiency of the traditional DP. Shape representation method using multiple open triangles is presented in this paper. Performance evaluation of corner-guided DP on this shape representation based on human relevance judgment is presented. This paper also presents the implementation and performance of the retrieval system. The retrieval system consists of a user friendly graphical user interface (GUI) which has been developed for testing. All the shape matching methods that have been developed have been integrated into the system for the user to choose during a retrieval process. The retrieval results are ranked from the most similar to the least similar and can be all viewed by the user.
Fish migration is being monitored year round to provide valuable information for the study of behavioral responses of fish to environmental variations. However, currently all monitoring is done by human observers. An automatic fish recognition and migration monitoring system is more efficient and can provide more accurate data. Such a system includes automatic fish image acquisition, contour extraction, fish categorization, and data storage. Shape is a very important characteristic and shape analysis and shape matching are studied for fish recognition. Previous work focused on finding critical landmark points on fish shape using curvature function analysis. Fish recognition based on landmark points has shown satisfying results. However, the main difficulty of this approach is that landmark points sometimes cannot be located very accurately. Whole shape matching is used for fish recognition in this paper. Several shape descriptors, such as Fourier descriptors, polygon approximation and line segments, are tested. A power cepstrum technique has been developed in order to improve the categorization speed using contours represented in tangent space with normalized length. Design and integration including image acquisition, contour extraction and fish categorization are discussed in this paper. Fish categorization results based on shape analysis and shape matching are also included.
An overview of the oyster industry in the U. S. with emphasis in Virginia shows oyster grading occurs at harvest, wholesale and processing markets. Currently whole oysters, also called shellstock, are graded manually by screening and sorting based on diameter or weight. The majority of oysters harvested for the processing industry are divided into three to four main grades: small, medium, large, and selects. We have developed a shape analysis method for an automatic oyster grading system. The system first detects and removes poor quality oysters such as banana shape, broken shell, and irregular shapes. Good quality oysters move further into grades of small, medium and large. The contours of the oysters are extracted for shape analysis. Banana shape and broken shell have a specific shape flaw (or difference) compared to the ones with good quality. Global shape properties such as compactness, roughness, and elongation are suitable and useful to measure the shape flaw. Image projection area or length of the major axis measured as global properties for sizing. Incorporating a machine vision system for grading, sorting and counting oysters supports reduced operating costs. The savings produced from reducing labor, increasing accuracy in size, grade and count and providing real time accurate data for accounting and billing would contribute to the profit of the oysters industry.
Efficient content-based image retrieval (CBIR) of biomedical images is a challenging problem. Feature representation algorithms used in indexing medical images on the pathology of interest have to address conflicting goals of reducing feature dimensionality while retaining important and often subtle biomedical features. In case of the vertebra, its shape effectively describes various pathologies identified by medical experts as being consistently and reliably found in x-rays in the image collection. A suitable shape method must enable retrieval relevant to the pathology in question. An approach to enabling pathology based retrieval is to use partial shape matching techniques. This paper describes our research in the development of such methods and initial retrieval results and related issues. The research is a part of our ongoing effort in developing CBIR for digitized images of a collection of 17,000 cervical and lumbar spine x-rays taken as a part of the second National Health and Nutrition Examination Survey (NHANES II) at the Lister Hill National Center for Biomedical Communications, an intramural R&D division of the U.S. National Library of Medicine.