KERNEL SPARSE MODELS FOR AUTOMATED TUMOR SEGMENTATION

摘要

A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

主权项

1. A method of segmenting a tumor region in an image, the method being implemented via execution of computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules, the method comprising:
computing a kernel sparse code for each pixel of at least a portion of the image; and identifying, using a classifier, each pixel belonging to the tumor region.