Hironobu Omatsu

Extraction algorithm of pulmonary fissures from thin-section CT images based on linear feature detector method

Describes a new automatic extraction algorithm of the pulmonary major and minor fissures from three-dimensional (3-D) chest thin-section images of helical computed tomography (CT). These fissures are used for the analysis of pulmonary conformation and the diagnosis of lung cancer. This algorithm consists mainly of the correction and the emphasis of a 2-D linear shadow. The authors applied the proposed algorithm to 25 sets of CT examinations of 12 patients. The results showed that major and minor fissures can be extracted by the proposed algorithm, without reference to streak artifacts on axial CT images by the beam hardening effect, and the motion artifacts by the cardiac beat.

Phase I and Pharmacokinetic Study of Paclitaxel by 24‐Hour Intravenous Infusion

Paclitaxel, a new antitubular agent, appears to be one of the most promising single agents for the chemotherapy of various solid tumors. The primary objectives of this phase I study of paclitaxel using 24‐h continuous intravenous infusions were to determine the maximum tolerated dose of paclitaxel administered by this schedule to Japanese patients with solid tumors and to evaluate the pharmaco‐kiiietics of paclitaxel. Eighteen patients received one of five doses of paclitaxel, 49.5, 75, 105, 135 or 180 mg/m2. Prcmedication with diphenhydramine, dexamethasone, and ranitidine was used to prevent acute hypersensitivity reactions. Pharmacokinetic data were obtained from all 18 patients. Dose‐limiting toxicities observed at 180 mg/m2 consisted of grade 4 granulocytopenia associated with grade 3 infection. No severe HSRs or cardiac toxicity were detected. Reversible toxicities observed included liver dysfunction, alopecia, peripheral neuropathy and myalgias. Pharmacokinetic studies performed using high‐performance liquid chromatography demonstrated that plasma concentrations of paclitaxel increased during the 24‐h infusion and declined immediately upon cessation of the infusion with a half life of 13.1‐24.6 h (75‐180 mg/m2). Less than 10% of paclitaxel was excreted in the urine within 72 h. The peak plasma concentrations and the areas under the concentration‐versus‐time curves increased linearly with the dose administered. Antitumor activity was observed in one patient with pulmonary metastasis from pharyngeal cancer. Based on these studies a phase II trial dose of 135 mg/m2 administered over 24 h was chosen.

Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images

We are developing a computerized approach to characterize pulmonary nodules through quantitative analysis between sequential 3-D thoracic images. In this approach the registration procedure of sequential 3-D pulmonary images consisted of two transformation steps: the rigid transformation step between two sequential 3-D thoracic CT images and the affine transformation step between two sequential region-of-interest (ROI) images including the pulmonary nodule. In both transformation step, the normalized mutual information was used as a voxel-based similarity measure. After the registration procedure, the 3-D pulmonary nodule image was segmented from the ROI image by a deformable surface method. The curvatures of each voxel in the nodule were computed directly from the gray-level 3-D image. Through curvatures a local description of the lesion was obtained by using shape index, curvedness, and CT value. Based on this local description of the nodule, the evolution of geometrical parameters was tracked through the time interval. Additionally, to characterize globally the evolution of the local description, the shape and the curvedness spectra were introduced. The interval changes of the lesion were traced in the feature spaces. The application results of our method to the sequence of 3-D thoracic images demonstrated that the interval changes of pulmonary nodules could be made visible.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Extraction of pulmonary fissures from thin-section CT images using calculation of surface-curvatures and morphology filters

This paper present an automatic extraction algorithm of the pulmonary major and minor fissures from three-dimensional (3-D) chest thin-section computed tomography (CT) images of helical CT. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. The proposed algorithm improves on the previous extraction method using the surface-curvatures calculation for density profile and morphological filters. The proposed method can extract the major and minor fissures in contact with the nodule and the chest walls. We apply the proposed algorithm to 12 patients. The results of our method are more accuracy to extract fissures around pulmonary lesions than by the previous method. The warped fissures extracted by our method show that lesions near fissures are malignant. Extracted fissures will aid in the diagnosis of lung cancer and in the analysis of automatic pulmonary conformation by using a computer.

ROI extraction of chest CT images using adaptive opening filter

We have already developed a prototype of computer-aided diagnosis (CAD) system that can automatically detect suspicious shadows from Chest CT images. But the CAD system cannot detect Ground-Grass-Attenuation perfectly. In many cases, this reason depends on the inaccurate extraction of the region of interests (ROI) that CAD system analyzes, so we need to improve it. In this paper, we propose a method of an accurate extraction of the ROI, and compare proposed method to ordinary method that have used in CAD system. Proposed Method is performed by application of the three steps. Firstly we extract lung area using threshold. Secondly we remove the slowly varying bias field using flexible Opening Filter. This Opening Filter is calculated by the combination of the ordinary opening value and the distribution which CT value and contrast follow. Finally we extract Region of Interest using fuzzy clustering. When we applied proposal method to Chest CT images, we got a good result in which ordinary method cannot achieve. In this study we used the Helical CT images that are obtained under the following measurement: 10mm beam width; 20mm/sec table speed; 120kV tube voltage; 50mA tube current; 10mm reconstruction interval.

Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images

This paper presents a method to analyze volume evolutions of pulmonary nodules for discrimination between malignant and benign nodules. Our method consists of four steps; The 3D rigid registration of the two successive 3D thoracic CT images, the 3D affine registration of the two successive region-of-interest (ROI) images, non rigid registration between local volumetric ROIs, and analysis of the local displacement field between successive temporal images. In preliminary study, the method was applied to the successive 3D thoracic images of two pulmonary lesions including a metastasis malignant case and an inflammatory benign to quantify the evolving process in the pulmonary nodules and surrounding structure. The time intervals between successive 3D thoracic images for the benign and malignant cases were 120 and 30 days, respectively. From the display of the displacement fields and the contrasted image by the vector field operator based on the Jacobian, it was observed that the benign case reduced in the volume and the surrounding structure was involved into the nodule in the evolution process. It was also observed that the malignant case expanded in the volume. These experimental results indicate that our method is a promising tool to quantify how the lesions evolve their volume and surrounding structures.

Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic images. This paper focuses on an approach for characterizing the internal texture which is one of important clues for differentiating between malignant and benign nodules. In this approach, each voxel was described in terms of shape index derived from curvatures on the voxel. The voxels inside the nodule were aggregated via shape histogram to quantify how much shape category was present in the nodule. Topological features were introduced to characterize the morphology of the cluster constructed from a set of voxels with the same shape category. The properties such as curvedness and CT density were also built into the representation. We evaluated the effectiveness of the topological and histogram features extracted from 3-D pulmonary nodules for classification of malignant and benign internal structures. We also compared the performance of the computerized classification with the experienced physicians. The classification performance based on the combined feature space reached the performance of the experienced physicians. Our results demonstrate the feasibility of using topological and histogram features for analyzing internal texture to assist physicians in making diagnostic decisions.

Computer-aided differential diagnosis of pulmonary nodules based on a hybrid classification approach

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in 3D thoracic CT images. Internal structure features were derived form CT density and 3D curvatures to characterize the inhomogeneous of CT density distribution inside the nodule. In the classification step, we combined an unsupervised k-means clustering (KMC) procedure and a supervised linear discriminate (LD) classifier. The KMC procedure classified the sample nodules into two classes by using the mean CT density values for two different regions such as a core region and a complement of the core region in 3D nodule image. The LD classifier was designed for each class by using internal structure features. The forward stepwise procedure was used to select the best feature subset from multi-dimensional feature spaces. The discriminant scores output form the classifier were analyzed by receiver operating characteristic (ROC) method and the classification accuracy was quantified by the area, Ax, under the ROC curve. We analyzed a data set of 248 pulmonary nodules in this study. The hybrid classifier was more effective than the LD classifier alone in distinguishing malignant and benign nodules. The improvement was statistically significant in comparison to classification in the LD classifier alone. The results of this study indicate the potential of combining the KMC procedure and the LD classifier for computer-aided classification of pulmonary nodules.

Bias field correction of chest thin section CT images

Helical computed tomography (CT) is a promising tool for the early diagnosis of lung cancer. The three-dimensional information makes it possible to detect a subtle change in any field of the lung. However, the diagnostic procedure is time-consuming, since a considerable number of images have to be reviewed in one examination. In order to lessen the burden to the reviewing physician and to improve the accuracy of diagnosis, we are developing a computer system, by which shadows of diagnostic importance can be highlighted among a number of nuisance changes. In particular, the peripheral blood vessels are analyzed with a special focus on the changes caused by lung cancer. We developed a computer algorithm, by which pulmonary blood vessels are extracted after removing the background bias. The comparison between the computer algorithm and an expert physicians reading showed a good agreement. Furthermore, this system can provide temporal changes in blood vessels, which are extremely important in diagnosis.

Extraction of pulmonary fissures from HRCT images based on surface curvatures analysis and morphology filters

The objective of the present paper is to extract the pulmonary major and minor fissures from 3D chest thin-section computed tomography (CT) images obtained by helical scan. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. We have proposed fissures extraction method without reference to streak artifacts and motion artifacts on the CT images. The new proposed algorithm improves on the previous extraction method using the surface-curvatures analysis for density profile and the morphological filters. The proposed method can also extract pulmonary fissures in contact with the module and the chest walls. We applied the proposed algorithm to 12 patients. The results of our method were more accuracy to extract fissures around pulmonary lesion than by the previous method. The warped fissures extracted by our method show that lesion near fissures is malignancy. Extracted fissures will be aided to diagnose lung cancer and to analyze automatically pulmonary conformation by using computer.