M. Kusumoto

An extraction algorithm of pulmonary fissures from multislice CT image

Aging and smoking history increases number of pulmonary emphysema. Alveoli restoration destroyed by pulmonary emphysema is difficult and early direction is important. Multi-slice CT technology has been improving 3-D image analysis with higher body axis resolution and shorter scan time. And low-dose high accuracy scanning becomes available. Multi-slice CT image helps physicians with accurate measuring but huge volume of the image data takes time and cost. This paper is intended for computer added emphysema region analysis and proves effectiveness of proposed algorithm.

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.

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.

3D analysis of solitary pulmonary nodules based on contrast enhanced dynamic CT

In this paper we propose three strategies for analysis of solitary pulmonary nodules in contrast enhanced dynamic CT images. First we collect contrasted images with regale time interval, and we analyze their CT values with respect to the time variation. In the second, we compare the shape spectra characteristic of each contrasted image. The third strategy consists of subtracting the noncontrasted images from contrasted images. The results show that each starkly promise good differentiation between benign and malignant nodule. So we try differential diagnosis.

Automatic slice image matching algorithm for comparative reading using data of the pulmonary structure

The lung cancer is very difficult to treat when condition of disease reaches an advanced stage. Therefore, the early detection and the early treatment by the mass cscreening are important. Now, the3 mass screening using the chest X-rays film is performed, and its detection rate is low. Recently, mass screening for lung cancer started using helical CT. However, since each subject has about 30 images, there is concern about the increase ofa burden to a physician. This comparative reading system solves difficulties of efficient display with the past and present images. But, automatic slice-image-matching is difficult by computer, since the states of the lungs at the time of photography differ from each other. This research analyzed change of the lungs between images with time and proposed automatic slice image mtching algorithm for comparative reading.

Computerized analysis of pulmonary nodules in topological and histogram feature spaces

This paper focuses on an approach for characterizing the internal structure which is one of important clues for differentiating between malignant and benign nodules in 3D thoracic images. 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. In the classification step, a hybrid unsupervised/supervised structure was performed to improve the classifier performance. It combined the k-means clustering procedure and the linear discriminate classifier. The receiver operating characteristics analysis was used to evaluate the accuracy of the classifiers. Our results demonstrate the feasibility of the hybrid classifier based on the topological and histogram features to assist physicians in making diagnostic decisions.

Image-based computer-aided prognosis of lung cancer: predicting patient recurrent-free survival via a variational Bayesian mixture modeling framework for cluster analysis of CT histograms

In this paper, we present a computer-aided prognosis (CAP) scheme that utilizes quantitatively derived image information to predict patient recurrent-free survival for lung cancers. Our scheme involves analyzing CT histograms to evaluate the volumetric distribution of CT values within pulmonary nodules. A variational Bayesian mixture modeling framework translates the image-derived features into an image-based risk score for predicting the patient recurrence-free survival. Using our dataset of 454 patients with NSCLC, we demonstrate the potential usefulness of the CAP scheme which can provide a quantitative risk score that is strongly correlated with prognostic factors.

Growth-rate estimation of pulmonary nodules in three-dimensional thoracic CT images based on CT density histogram analysis and its application to nodule classification

In research and development of computer-aided differential diagnosis using thoracic CT images, there is now widespread interest in the use of nodule doubling time for measuring the volumetric changes of pulmonary nodule. The evolution pattern of each nodule might depend on the CT density distribution pattern inside nodule such as pure GGO, mixed GGO, or solid nodules. This paper presents a computerized approach to measure nodule density variation inside small pulmonary nodule using CT images. The approach consists of five steps: (1) nodule segmentation, (2) computation of CT density histogram, (3) nodule categorization (α, β, γ, δ, and ε) based on CT density histogram, (4) computation of doubling time based on CT density histogram, and (5) classification between benign and malignant cases. Using our dataset of follow-up scans of pulmonary nodules, we evaluated evaluation patterns of nodules on the basis of the predominant five nodule categorizations and designed the classification approach between benign and malignant cases. The preliminary experimental result demonstrated that our approach has a potential usefulness to assess the nodule evolution using thoracic CT images.

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

The purpose of this study is to develop an image-guided decision support system that assists decision-making in clinical differential diagnosis of pulmonary nodules. This approach retrieves and displays nodules that exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. In order to build the system, there are following issues that should be solved: 1) to categorize the nodule database with respect to morphological and internal features, 2) to quickly search nodule images similar to an indeterminate nodule from a large database, and 3) to reveal malignancy likelihood computed by using similar nodule images. Especially, the first problem influences the design of other issues. The successful categorization of nodule pattern might lead physicians to find important cues that characterize benign and malignant nodules. This paper focuses on an approach to categorize the nodule database with respect to nodule shape and CT density patterns inside nodule.

CAD system for lung cancer CT screening

Lung Cancer is know as one fo the most difficult cancers to cure. The detection of lung cancer in its early stage can be helpful for medical treatment to danger. However, mass screening based on helical CT images brings a considerable number of images to diagnosis, the time-consuming fact makes it difficult to be used in the clinic. To increase the efficiency of the mass screening process, we developed a Computer-Aided Diagnosis (CAD) system, which can detect nodules at high speed. It takes 17 seconds per case (35 images) to detect nodules. In this paper, we describe the development of this CAD system and specifications.