Gentaro Fukano

Recognition method of lung nodules using blood vessel extraction techniques and 3D object models

In this paper, we propose a method for reducing false positives in X-ray CT images using ridge shadow extraction techniques and 3D geometric object models. Suspicious shadows are detected by our variable N-quoit (VNQ) filter, which is a type of mathematical morphology filter. This filter can detect lung cancer shadows with the sensitivity over 95[%], but it also detects many false positives which are mainly related to blood vessel shadows. We have developed two algorithms to distinguish lung nodule shadows from blood vessel shadows. In the first algorithm, the ridge shadows, which come from blood vessels, are emphasized by our Tophat by Partial Reconstruction filter which is also a type of mathematical morphology filter. And then, the region of the ridge shadow is extracted using binary distance transformation. In the second algorithm, we propose a recognition method of nodules using 3D geometric lung nodule and blood vessel models. The anatomical knowledge about the 3D structures of nodules and blood vessels can be reflected in recognition process. By applying our new method to actual CT images (37 patient images), a good result has been acquired.

Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method

We previously proposed a recognition method of lung nodules based on the experimentally selected feature values (such as contrast, circularities, etc.) of pathological candidate regions detected by our Quoit filter. In this paper, we propose a new recognition method of lung nodule using each CT value itself in ROI (region of interest) area as a feature value. In the clustering stage, the pathological candidate regions are first classified into some clusters using the principal component (PC) theories. A set of CT values in each ROI is regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by applying the principal component analysis (PCA). The eigen vectors (we call them eigen images) corresponding to the 10 largest eigen values, are utilized as base vectors for subspaces of the clusters in the feature space. In the discrimination stage, correlations are measured between the testing feature vector and the subspace which is spanned by the eigen images. If the correlation with the abnormal subspace is large, the pathological candidate region is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method, good results have been acquired.

Recognition of lung nodules from x-ray CT images considering 3D structure of objects and uncertainty of recognition

In this paper, we propose a method of recognition of lung nodules using 3D nodule and blood vessel models considering uncertainty of recognition. Region of interest (ROI) areas are extracted by our quoit filter which is a kind of Mathematical Morphology filter. We represent nodules as sphere models, blood vessels as cylinder models and the branches of the blood vessels as the connections of the cylinder models, respectively. All of the possible models for nodules and blood vessels are generated which can occur in the ROI areas. The probabilities of the hypotheses of the ROI areas coming from the sphere models are calculated and the probabilities for the cylinder models are also calculated. The most possible sphere models and cylinder models which maximize the probabilities are searched considering uncertainty of recognition. If the maximum probability for the nodule model is higher, the shadow candidate is determined to be abnormal. By applying this new method to actual CT images (37 patient images), good results have been acquired.

Eigen nodule: view-based recognition of lung nodule in chest x-ray CT images using subspace method

We previously proposed a recognition method of lung nodules based on experimentally selected feature values (such as contrast, circularities, etc.) of the suspicious shadows detected by our Quoit filter. In this paper, we propose a new recognition method of lung nodule using each CT value itself in ROI (region of interest) area as a feature value. In the clustering stage, first, the suspicious shadows are classified into some clusters using Principal Component (PC) theories. A set of CT values in each ROI is regarded as a feature vector, and then the eigen vectors and the eigen values are calculated for each cluster by applying Principal Component Analysis (PCA). The eigen vectors (we call them Eigen Images) corresponding to the first 10 largest eigen values, are utilized as base vectors for subspaces of the clusters in the feature space. In the discrimination stage, correlations are measured between the unknown shadow and the subspace which is spanned by the Eigen Images. If the correlation with the abnormal subspace is large, the suspicious shadow is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method, good results have been acquired.