Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease

A review of intelligent content-based indexing and browsing of medical images

Physicians are beginning to be able to gain access, through the Internet, to the world's collections of multimedia medical information such as MRI (magnetic resonance imaging) and CT (computer tomography) image archives, videos of surgical operations and medical lectures, textual patient records and media annotations. New techniques and tools are needed to represent, index, store and retrieve digital content efficiently across large collections. In this review, we trace the development of visual information systems for healthcare and medicine from Picture Archiving and Communications Systems (PACS) to the recent advances in content-based image retrieval, whereby images are retrieved based on their visual content similarity - that is, colour, texture, and shape. Medical images, unlike consumer-oriented images, pose additional challenges to content-based image retrieval, in that visual features of normal and pathological images are typically separated by only subtle differences in visual appearance. Intelligent image retrieval and browsing therefore requires a combination of prior knowledge of the medical domain, image content and image annotation analysis. To this end, we also overview theI-Browse project, conducted jointly by the Clinical School of the University of Cambridge and the City University of Hong Kong, which aims to develop techniques which enable a physician to search over image archives through a combination of semantic and iconic contents

Use of fetal-pelvic index in the prediction of vaginal birth following previous cesarean section

Aim: To clarify the usefulness of the fetal-pelvic index as a predictor of vaginal birth after previous lower segment cesarean section. Methods: One hundred and seventy women with one lower segment cesarean section who attempted for trial of vaginal birth were enrolled. Pelvimetry was performed to measure maternal pelvic inlet and mid-cavity circumferences at 37 weeks gestation. Ultrasound was performed to measure fetal head and abdominal circumferences at 38-39 weeks. The fetal-pelvic index was derived. The predictability of fetal-pelvic index in the predicting the outcome of delivery was calculated. Results: Fifty-seven (33.5%) women required repeated cesarean section and 113 (66.5%) delivered vaginally. Twenty-two women with positive fetal-pelvic index had repeated cesarean section. The predictability of positive fetal-pelvic index was 48.9%. Ninety of the 125 patients with a negative fetal-pelvic index delivered vaginally. The predictability of negative fetal-pelvic index was 72.0%. Conclusions: Fetal-pelvic index derived in the antenatal period has low predictive value in predicting of successful vaginal birth after cesa-rean section. This index is not useful in clinical practice.link_to_subscribed_fulltex

Antibiotic prophylaxis in total abdominal hysterectomy: A case-control study

A retrospective case-control study was conducted to evaluate the use of prophylactic antibiotics in the prevention of serious postoperative infection in patients undergoing elective total abdominal hysterectomy between 1 January 1992 and 30 December 1995. A total of 439 women had a total abdominal hysterectomy during this period. Forty-three percent of the patients were given prophylactic cefotaxime before the procedure. Cefotaxime was chosen because it is a broad spectrum antibiotic and has been proven to be effective in reducing postoperative morbidity. A single dose was adequate in reducing surgical wound breakdown (0 vs 6, p < 0.05), urinary tract infection (5 vs 21, p < 0.01) and hospital stay (7 vs 9, p < 0.05); fewer patients required additional antibiotic therapy (27 vs 47, p < 0.05) and none developed superinfection.link_to_subscribed_fulltex

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. © 2000 IEEE