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Biomedical subjects

Noboru Niki

Publications and source records attributed to Noboru Niki.

6 recordsLinked to original sources
PubMed2003

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

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.

Databases, Factual↗
PubMed2002

Cancerous nuclei detection on digitized pathological lung color images.

In this paper, we propose a methodology (in the form of a software package) for automatic extraction of the cancerous nuclei in lung pathological color images. We first segment the images using an unsupervised Hopfield artificial neural network classifier and we label the segmented image based on chromaticity features and histogram analysis of the RGB color space components of the raw image. Then, we fill the holes inside the extracted nuclei regions based on the maximum drawable circle algorithm. All corrected nuclei regions are then classified into normal and cancerous using diagnostic rules formulated with respect to the rules used by experimented pathologist. The proposed method provides quantitative results in diagnosing a lung pathological image set of 16 cases that are comparable to an expert's diagnosis.

Adenocarcinoma↗
PubMedDate not supplied

Development of a novel computer-aided diagnosis system for automatic discrimination of malignant from benign solitary pulmonary nodules on thin-section dynamic computed tomography.

OBJECTIVES: As an application of the computer-aided diagnosis of solitary pulmonary nodules (SPNs), 3-dimensional contrast-enhanced (CE) dynamic helical computed tomography (HCT) was performed to evaluate temporal changes in the internal structure of nodules to differentiate between benign nodules (BNs) and malignant nodules (MNs). METHODS: There were 62 SPNs (35 MNs and 27 BNs) included in this study. Scanning (2-mm collimation) was performed before and 2 and 4 minutes after CE dynamic HCT. The CT data were sent to a computer, and the pixels inside the nodule were characterized in terms of 3 parameters (attenuation, shape index, and curvedness value). RESULTS: Based on the CT data at 4 (MN: 1.81-27.1, BN: -42.8 to -3.29) minutes after CE-dynamic HCT, a score of 0 or higher can be assumed to indicate an MN. CONCLUSIONS: Three-dimensional computer-aided diagnosis of the internal structure of SPNs using CE dynamic HCT was found to be effective for differentiating between BNs and MNs.

Adult↗
PubMedDate not supplied

Computer-simulation technique for low dose computed tomographic screening.

OBJECTIVES: The purpose of this study is to assess the relative influence of noise and artifact in detecting lung nodules on low dose computed tomographic (CT) screening. METHODS: We develop the computer-simulation technique that allows tube current simulation and virtual nodule insertion in any CT images. The tube current simulation uses a reduction model that adds random Gaussian noise distribution to existing projection data. The virtual nodules are generated using a dedicated CT simulation tool with same scanner geometry. RESULTS: The coefficient of the correlations between the contrast-to-noise ratio of the actual scan and simulated tube current images was 0.98. There was no difference in CT number between virtual nodules and actual nodules [t test results = 0.60, t50(0.01) = 2.70 at 10 mA] and the coefficient of the correlations of the image noise was 0.99. CONCLUSIONS: Our technique is useful for systematic evaluation of radiation dose reduction and structure visibility in low-dose CT screening.

Computer Simulation↗
PubMedDate not supplied

A computer-aided diagnosis (CAD) system in lung cancer screening with computed tomography.

We evaluated a computer-aided diagnosis (CAD) system with automatic detection of pulmonary nodules for lung cancer screening with computed tomography (CT). Five hundred and eighteen participants were examined with low-dose helical CT during a lung cancer screening by three respiratory physicians according to the General Rule edited by the Japan Lung Cancer Society. Four cases were detected by CAD and pathologically diagnosed as lung cancer. We compared the detection capability of the physician and CAD in 301 participants. Three physicians determined 75/301 (24.9%) participants as "e" (suspicious of lung cancer) in consensus without CAD, while 3 participants were added to "e" with CAD. Three physicians did not independently judge as "e" in 14 (18.7%), 16 (21.3%) and 16 (21.3%) out of 75 participants. CAD could not identify 17 (22.7%) nodules of 75 participants, and all 17 were less than 6 mm in diameter. The CAD system offers a useful second opinion when physicians examine patients at lung cancer CT screenings.

Adult↗