Time and space results of dynamic texture feature extraction in MR and CT image analysis

A three-object model for the similarity searches of chest CT images

We propose in this paper a three-object model specifically for the archiving and retrieval of chest CT images. To calculate parameters for the model, each chest CT image needs to be processed to segment the three main objects and then the features be extracted to describe the objects' properties and relationships. In the image segmentation part, we applied the knowledge of the modality on chest CT images and modified the traditional watershed image segmentation algorithm including a four-step merging algorithm specifically for chest CT images. After segmentation, the mediastinum and two lung lobes are identified. The mediastinum object is mainly described by shape-related features while the two lung lobes are described mainly by texture features. A three-object model was exploited to describe the object features and the spatial relationship among objects. To test the capability of the three-object model to the similarity searches of chest CT images, we developed a CBIR system in which three distinct query modes were provided. They are 'searching by ARGs', 'searching by shape features of mediastinum', and 'searching by texture features of lung lobes'. The experimental results show that the three-object model demonstrates impressive power in the similarity searching of chest CT images. Among the three searching modes, the 'searching by shape features of mediastinum' and 'searching by texture features of lung lobes' modes provide user choices to search for images with high similarities in specific objects rather than in the whole images. The precision rate of either query mode is high, with an average of around 80% out of the first 30 result images are justified as similar, which is impressive in a fully automatic image query system using content features. Nevertheless, the two query modes that concentrate on distinct object features show slightly better capability in searching for similar images than the 'searching by ARGs' mode.

Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images

Intravascular Ultrasound (IVUS) is a diagnostic imaging technique that provides tomographic visualization of coronary arteries. The aim of this study was to evaluate five texture analysis techniques and determine their ability to distinguish between plaque lesions of different composition. Using histological correlation, regions of calcified, fibrous, and necrotic core plaque were chosen from 27 coronary plaques. First-order statistics, Haralick's method, Laws' texture energy method, the neighborhood gray-tone difference matrix method, and texture spectrum features were examined using discriminant analysis. Self-validation indicated that Haralick's method yielded the most accurate results, with resubstitution and cross-validation error rates of 0.00 and 14.76%, respectively. Further optimization gave error rates of 6.67%, using only two discriminating features, IDM and entropy.