Texture analysis of technegas lung ventilation images.

Technegas lung ventilation images sometimes have 'hot spots', particularly in patients with respiratory disease. A novel technique is presented for quantifying this 'spottiness' using morphological texture analysis. A set of 32 images from patients with various respiratory diseases is studied. Images are filtered at a range of scales using morphological opening, and the slopes of image metrics versus structuring element size are used as texture parameters. The results are compared with the opinions of three experienced nuclear medicine physicians who have classified the images into two groups, 'spotty' and 'non-spotty', and have ranked the former. For the spotty images, the computer and observer ranks are compared; the highest correlation is rs = 0.66 (p = 0.01) for a single parameter, and rs = 0.71 (p < 0.01) for a combination of two parameters. Using a pair of parameters, 83% and 90% correct classification rates are obtained for the spotty and non-spotty classes, respectively. It is concluded that these texture parameters provide a useful measure of image spottiness, and it is demonstrated that this technique is superior to previously published methods. The practical value of the technique is illustrated using two applications.