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Visible human data has been widely used in various medical research and computer science applications. We present a new application for this data: a method to classify which body segment a transverse cross section image belongs to. The labeling of the data is created with the guidance of an online body cross section tutorial. The visual properties of the images are represented using a variety of feature descriptors. To avoid problems that arise from the large dimensionality of features, feature selection is applied. The multi-class SVM is employed as the classifier. Both the CT scans and the color photographs of cryosections of the whole body (male and female) are used to test the proposed method. The high performance with overall accuracy above 98% on both the 2160 CT dataset and the 1870 cryosectional photos show the method is very promising. Because of its observed effectiveness on visible human data, we will extend our approach to classify figures in biomedical articles.

We present a generalized line histogram technique to compute global rib-orientation for detecting rotated lungs in chest radiographs. We use linear structuring elements, such as line seed filters, as kernels to convolve with edge images, and extract a set of lines from the posterior rib-cage. After convolving kernels in all possible orientations in the range [0, p), we measure the angle for which the line histogram has maximum magnitude. This measure provides a good approximation of the global chest rib-orientation for each lung. A chest radiograph is said to be upright if the difference between the orientation angles of both lungs with respect to the horizontal axis, is negligible. We validate our method on sets of normal and abnormal images and argue that rib orientation can be used for rotation detection in chest radiographs as aid in quality control during image acquisition, and to discard images from training and testing data sets. In our test, we achieve a maximum accuracy of 90%.

Graphical illustrations are often used in biomedical articles to convey statistical results, schematics, etc. They are frequently accompanied with superimposed text annotations, or figure-text. It is generally assumed that this figure-text provides information that complements associated textual metadata, such as the captions, or bibliographic citations for indexing the figures and enhanced retrieval quality. However, to the best of our knowledge nothing in the literature adequately supports the assumption of information gain. In this article, we report the results of a pilot study that compares image retrieval based on figure captions alone to that using figure-text in addition to captions. In the blind study two judges evaluated a set of figures retrieved on the topic of "lung cancer". We find that figure-text could help improve retrieval performance for specific queries.

We propose a Markov random field (MRF)-based method to segment photographic biomedical images into three image sub-regions, viz., tissue, photo, and background. Segmentation results are then used to extract local and global visual features to separate images with tissue, such as endoscopic images, from general photographs.