Experimental evaluation of auditory display and sonification of textured images

Presented at the 4th International Conference on Auditory Display (ICAD), Palo Alto, California, November 2-5, 1997.In order to verify the potential of proposed auditory display and sonification methods for aural analysis of textured images, a set of experiments was designed and was presented to 10 subjects. The results obtained and limitations of the methods are discussed

A fourier domain directional filterng method for analysis of collagen alignment in ligaments

Collagen fibers and their component fibrils make up the protenaceous backbone of most tissues and provide the majority of their resistance to tensile loading. Spatial orientation of collagen fibrils is an important factor in determining tissue properties. This is particularly true in ligament tissue, since ligaments must be loose enough to allow joints to move but tight enough to prevent joint surfaces from separating. A method is presented here to reproducibly quantify this collagen arrangement, which should be useful in studies on ligament healing and growth

Auditory display and sonification of textured image

Presented at 3rd International Conference on Auditory Display (ICAD), Palo Alto, California, November 4-6, 1996

Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns

This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick’s texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography