Yoshiki Kawata

Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images1

RATIONALE AND OBJECTIVES An example-based assisting approach that supports decision making in classifying pulmonary nodules in 3-dimensional (3D) thoracic computed tomography images has been developed. MATERIALS AND METHODS The example-based assisting approach retrieves and displays nodules that exhibit morphologic and internal profiles consistent to the nodule in question. It uses a 3D computed tomography image database containing 143 pulmonary nodules for which diagnosis is known. The central module makes possible analysis of the query nodule image and extraction of the features of interest: shape, surrounding structure, and internal structure of the nodules. The principal axes and the compactness characterize the nodule shape. The surrounding and internal structures are represented by the distribution pattern of computed tomography density value and 3D curvature indexes. The nodule representation is then used for computing a similarity measure such as a correlation coefficient and a malignant likelihood of the query nodule. The malignant likelihood is estimated by the difference between the representation patterns of the query case and the retrieved lesions. The Mahalanobis distance was adopted as the difference measure. The approach performance was assessed through leave-one-out method by the false-positive rate. RESULTS Given a query nodule image, the proposed method retrieved benign and malignant images similar to the query case and provided its malignant likelihood. The number of cases that obtained enough number of the retrieved cases for estimating the malignant likelihood was 107 cases (malignant, 70; benign, 37) in our database. Sensitivity was 91.4% (64 of 70 malignant nodules), specificity was 51.4% (19 of 37 benign nodules), and accuracy values were 77.6% (83 of 107 nodules). CONCLUSION Preliminary assessment of this approach showed that an example-based assisting approach is an effective tool for making the diagnostic decision in the classification of pulmonary nodules using the nodule image database.

Classification of pulmonary nodules in thin-section CT images based on shape characterization

Shape characterization of small pulmonary nodules plays a significant role in differential diagnosis that discriminates malignant and benign nodules at early stages of pulmonary lesion development. This paper presents a method to characterize small pulmonary nodules based on the morphology of the development of lung lesions in thin-section CT images. The feature extraction algorithms are designed to extract the shape characteristic parameters from three-dimensional (3-D) nodule images using surface curvatures and ridge line. Experiments which show the feasibility of our method to improve the diagnostic accuracy are also demonstrated by applying the method to nodule images.

Lung cancer detection based on helical CT images using curved surface morphology analysis

Lung cancer is known as one of the most difficult cancers to cure. The detection of lung cancer in its early stage can be helpful for medical treatment to limit the danger. A conventional technique that assists the detection uses helical CT, which provides information of 3D cross sectional images of the lung. We expect that the proposed technique will increase diagnostic confidence. However, mass screening based on helical CT images leads to a considerable number of images for the diagnosis, this time-consuming fact makes it difficult to be used in the clinic. To increase the efficiency of the mass screening process, we had proposed a computer-aided diagnosis (CAD). In this paper, we describe lung cancer detection based on helical CT Images using curved surface morphology analysis. Firstly, we extract the lung area from the original image. Secondly, we compute shape index value of the lung area. Thirdly, we extract the ROI (Region Of Interest) from the computed shape index value. Finally, we apply the diagnosis rule using neural network and detect the suspicious regions. We show here the result of our algorithm which is applied to helical CT images of 390 patients.

Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival.

PURPOSE Quantification of the CT appearance of non-small cell lung cancer (NSCLC) is of interest in a number of clinical and investigational applications. The purpose of this work is to present a quantitative five-category (α, β, γ, δ, and ɛ) classification method based on CT histogram analysis of NSCLC and to determine the prognostic value of this quantitative classification. METHODS Institutional review board approval and informed consent were obtained at the National Cancer Center Hospital. A total of 454 patients with NSCLC (maximum lesion size of 3 cm) were enrolled. Each lesion was measured using multidetector CT at the same tube voltage, reconstruction interval, beam collimation, and reconstructed slice thickness. Two observers segmented NSCLC nodules from the CT images by using a semi-automated three-dimensional technique. The two observers classified NSCLCs into one of five categories from the visual assessment of CT histograms obtained from each nodule segmentation result. Interobserver variability in the classification was computed with Cohens κ statistic. Any disagreements were resolved by consensus between the two observers to define the gold standard of the classification. Using a classification and regression tree (CART), the authors obtained a decision tree for a quantitative five-category classification. To assess the impact of the nodule segmentation on the classification, the variability in classifications obtained by two decision trees for the nodule segmentation results was also calculated with the Cohens κ statistic. The authors calculated the association of recurrence with prognostic factors including classification, sex, age, tumor diameter, smoking status, disease stage, histological type, lymphatic permeation, and vascular invasion using both univariate and multivariate Cox regression analyses. RESULTS The κ values for interobserver agreement of the classification using two nodule segmentation results were 0.921 (P < 0.001) and 0.903 (P < 0.001), respectively. The κ values for the variability in the classification task using two decision trees were 0.981 (P < 0.001) and 0.981 (P < 0.001), respectively. All the NSCLCs were classified into one of five categories (type α, n = 8; type β, n = 38; type γ, n = 103; type δ, n = 112; type ɛ, n = 193) by using a decision tree. Using a multivariate Cox regression analysis, the classification (hazard ratio 5.64; P = 0.008) and disease stage (hazard ratio 8.33; P < 0.001) were identified as being associated with an increased recurrence risk. CONCLUSIONS The quantitative five-category classifier presented here has the potential to provide an objective classification of NSCLC nodules that is strongly correlated with prognostic factors.

Shape analysis of pulmonary nodules based on thin-section CT images

Shape characterization of small pulmonary nodules plays a significant role in differential diagnosis that discriminates malignant and benign nodules at early stages of pulmonary lesion development. This paper presents a method to characterize small pulmonary nodules based on the morphology of the development of lung lesions in thin section CT images. The feature extraction process focuses on the difference between the malignant and benign surface characteristics. Experiments to show its feasibility to improve the diagnostic accuracy are also demonstrated by applying the algorithm to eighteen cases including twelve malignant and six benign nodules.

Three-dimensional analysis of lung areas using thin slice CT images

The lung area has very complicated structure which consists of the bronchus, the pulmonary artery, and the pulmonary vein. So it is difficult for even medical doctors to understand the spatial relationships among the tumor, the bronchus, and the blood vessels. Here we present a 3D image analysis method of lung areas using thin slice CT images, and we apply this system to the differential diagnosis such as malignant or benign decision of the abnormal tissue. This system consists of two steps. The first step is the analysis of the structure of the lung area, and the second step is the visualization of classified pulmonary organs for quantitative analysis.

A 3‐D display method of fuzzy shapes obtained from medical images

A three-dimensional (3-D) display of medical images allows better perception of the continuity of structures and spatial relationships than a display of individual slices. These 3-D display methods consist of a contour extraction, a shape reconstruction and a shading. However, the boundary judgment of soft tissues, like a tumor, is difficult even for expert doctors. Especially, 3-D display depends largely on a result of the contour extraction. The 3-D display method is required to display the presentation of the continuity of organ shapes. This paper presents the development of a display method to preserve the continuity of organ shapes. The display method consists of the extraction of fuzzy shapes and their shading. The extraction of fuzzy shapes is executed by operation of the fuzzy c-means clustering algorithm on medical images and the use of a connectivity of an organ shape. The shading of fuzzy shapes uses a volume rendering, which is analyzed numerically and improved to realize a powerfully interactive 3-D display. The effectiveness of the proposed 3-D display method is shown using head MRI images.

Curvature‐based internal structure analysis of pulmonary nodules using thoracic 3D CT images

In qualitative diagnosis for discrimination between malignant and benign pulmonary nodules, the features contributing to the morphologies of existing structures, such as the internal structure and peripheral characteristics of the nodules, pulmonary blood vessels and bronchi become important. In this paper, we will characterize the internal structure of pulmonary nodules in terms of 3D curvature by using thoracic 3D CT images, which present valuable information for qualitative diagnosis, and will describe a technique for distinguishing between benign and malignant nodules. The 3D curvature calculation uses the method of isointensity surfaces, and the image elements of the nodule interior are represented locally by CT values as well as by the shape index and curvedness obtained from the 3D curvature. From these histograms, benign and malignant nodules are globally characterized, allowing their discrimination. Results of screening by physicians, using the pulmonary nodules and the discrimination results obtained by the proposed technique, are compared by using ROC curves and the effectiveness of the proposed technique is shown.

Computer-aided diagnosis for pulmonary nodules based on helical CT images

In this paper, the authors present a computer assisted automatic diagnostic system for lung cancer that detects nodule candidates at an early stage from helical CT images of the thorax. The diagnostic system consists of analytic and diagnostic procedures. In the analysis procedure, the authors extract the lung and the blood vessel regions using a fuzzy clustering algorithm, then they analyze the features of these regions. In the diagnosis procedure, the authors define diagnostic rules utilizing the extracted features which support the determination of the candidate nodule locations.

Automatic extraction of pulmonary fissures from multidetector-row CT images

The paper describes the extraction of pulmonary major and minor fissures from three-dimensional (3D) chest multidetector-row computed tomography (MDCT) images. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. We have proposed (see Kubo, M. et al, IEEE Trans. Nucl. Sci, vol.46, p.2128-33, 1999) an automatic fissures extraction method using thin-section CT images with much noise. The present study proposes a simpler algorithm to extract fissures using MDCT images with little noise. The new proposed algorithm consists of the highlight method using the VanderBrug operator and the extraction method using morphology filters. We applied the proposed algorithm to one patient. Our method could accurately extract fissures.