Computer analysis of echographic textures in Hashimoto disease of the thyroid.

Ultrasound B-scan images of the thyroid obtained from 10 patients with Hashimoto disease were digitized and processed by a computer method of image analysis that segments complex B-scan images into regions of homogeneous texture. The method was first applied to B-scan images of the normal thyroid and it consistently classified the normal tissue into a unique region. When applied to Hashimoto disease B-scan images, the same method segmented the thyroid into two regions. Detailed analysis of these regions revealed that their gray-level histograms were very different from that of the normal thyroid in eight cases. In two cases the histogram of one of the regions was similar to that of the previous eight cases, whereas the histogram and the tissue of the other region were similar to those of the normal tissue. This paper shows how these results can be interpreted according to the natural history of the Hashimoto disease.

Local histogram information content of ultrasound B-mode echographic texture.

In the diagnostic ultrasound community, the echographic B-scan texture is an important area of investigation since it can be analyzed to characterize the histologic state of internal tissues. In the present paper, a minicomputer based system was used to digitize B-mode images and to develop a method to measure their textural information. This method is based on the concept of local information content of spatial image proposed by Lowitz (1983, 1984). It first measures the local gray-level histogram in a small square window centered on each picture element (pixel) of a digitized B-mode image. The information derived from the local histograms is then used to characterize the tissues, to partition the B-mode image into homogeneous zones of texture, to estimate to what extent a tissue is different from another, to delimit the contours of a tissue and to measure its surface. The method is illustrated on the thyroid gland but it can be applied to the study of other organs.

Texture analysis of ultrasound B-mode images by segmentation.

B-mode texture characterization by supervised methods of pattern recognition is subject to the following drawbacks: precise localization of the lesion to characterize is often difficult and, even when the lesion is well isolated, its texture can be corrupted by the presence of tumor non specific structures. These structures are not easily discernable and introduce a bias in the statistical measures. The results presented in this paper show that these problems can be circumvented by the use of an unsupervised method of image segmentation. The method enhanced the B-mode image and partitions the non specific structures and the lesion texture in different regions which can be characterized independently by statistical methods. The unsupervised approach also facilitates the clinical diagnosis done by visual inspection, by revealing subtle characteristics of the B-mode textures.