This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by
lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a
secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A
telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which
is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our
system considering human machine interface and security. It consists of data entry terminals, a database server, a computer
aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication
in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution.
We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese
next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of
data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution
of screening images and cooperative reading and that the encryption and image distribution methods we proposed
were applicable to the encryption and distribution of general DICOM images via the Internet.
In our previous method, thresholding for feature extraction was one of the biggest problems. We have developed two methods for obtaining the optimal threshold, but they are not enough to apply to all the cancers in CT. In this paper, we prepared about 9 thresholds and extracted features by using these thresholds. Then we applied a discriminate method using a subspace derived from the extracted feature metrices.
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.
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.
In this paper, we described an algorithm of automatic detection of Ground Glass Opacities (GGO) from X-ray CT images. In this algorithm, first, suspicious shadows are extracted by our Variable N-Quoit (VNQ) filter which is a type of Mathematical Morphology filters. This filter can detect abnormal shadows with high sensitivity. Next, the suspicious shadows are classified into a certain number of classes using feature values calculated from the suspicious shadows. In our traditional clustering method, a medical doctor has to manually classify the suspicious shadows into 5 clusters. The manual classification is very hard for the doctor. Thus, in this paper, we propose a new automatic clustering method which is based on a Principal Component (PC) theory. In this method, first, the detected shadows are classified into two sub-clusters according to their sizes. And then, each sub-cluster is further classified into two sub-sub-clusters according to PC Scores(PCS) calcuated from the feature values of the shadows in the sub-cluster. In this PCS-based classification, we use a threshold which maximizes the distance between the two sub-sub-clusters. The PCS-based classification is iterated recursively. Using discriminate functions based on Mahalanobis distance, the suspicious shadows are determined to be normal or abnormal. This method was examined by many samples (including GGO's shadows) of chest CT images, and proved to be very effective.
In this paper we propose a new recognition method of lung nodules from x-ray CT images using 3D Markov random field (MRF) models. Pathological shadow candidates are detected by our Quoit filter which is a kind of mathematical morphology filter, and volume of interest (VOI) areas which include the shadow candidates are extracted. The probabilities of the hypotheses that the VOI areas come from nodules (which are candidates of cancers) and blood vessels are calculated using nodule and blood vessel models evaluating the relations between these object models using 3D MRF models. If the probabilities for the nodule models are higher, the shadow candidates are determined to be abnormal. Otherwise, they are determined to be normal. Experimental results for 38 samples (patients) are shown.
In this paper, we described an algorithm of automatic detection of ground glass opacities (GGO) from X-ray CT images. In this algorithm, at first, pathological shadow candidates are extracted by our variable N-Quoit filter which is a kind of mathematical morphology filter. Next, shadow candidates are classified into some classes using feature values calculated from the shadow candidates. By using discriminate functions, at last, shadow candidates are discriminated between normal shadows and abnormal ones. This method was examined by 38 samples (including GGO's shadows) of chest CT images, and proved to be very effective.
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.
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