Recognition of lung nodule shadows from chest X‐ray CT images using 3D Markov random field models

Abstract

SUMMARYThis paper presents a scheme for recognizing nodule shadows that are candidate lung cancer lesions from chest X-ray CT images. In recognizing shadow candidates, their surrounding shadows give useful information. The proposed scheme recognizes nodule shadows by formulating the relationships between shadow candidates and their surrounding shadows in the framework of a three-dimensional Markov random field model. First, it detects isolated shadows that become nodule candidates, extracts a volume of interest that includes shadow candidates, and divides the volume of interest into rectangular regions. It expresses nodules and blood vessels in three-dimensional geometric models of spheres and cylinders, and lists a number of object models having high probabilities of existing in each rectangular region. It then generates a combination of object models by assembling the physical models. It formulates the relationships between each rectangular region, anatomical verifications of the object model combinations and the degree of agreement between observed CT images and object model combinations in the framework of a three-dimensional Markov random field model, and computes the probability that a shadow candidate is a nodule and the probability that the shadow candidate is not a nodule. A shadow candidate is determined to be a nodule if the probability of it being a nodule is high, while it is determined to be normal otherwise. The proposed scheme is applied to actual CT images of 10-mm slice intervals to show its efficacy.

Image processing algorithm of computer-aided diagnosis in lung cancer screening by CT

“…(12) is 0, and a large Q output is obtained from Eq. (10). Figure 9(b), on the other hand, represents the case in which a cylindrical vessel is input.…”