Mitsuru Kubo

Computer aided diagnosis system for lung cancer based on helical CT images

In this paper we describe a computer assisted automatic diagnosis system for lung cancer that detects tumor candidates at an early stage from helical computerised tomographic (CT) images. This automation of the process reduces the time complexity and increases the diagnosis confidence. Our algorithm consists of an analysis part and a diagnosis part. In the analysis part, we extract the lung and pulmonary blood vessel regions and analyze the features of these regions using image processing techniques. In the diagnosis part, we define diagnosis rules based on these features, and detect tumor candidates using these rules. We have applied our algorithm to 450 patients data for mass screening. The results show that our algorithm detected lung cancer candidates successfully.

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.

Computer Aided Screening System for Lung Cancer Based on Helical CT Images

In this paper, we describe a computer assisted automatic diagnosis system for lung cancer that detects tumor candidates at an early stage from helical CT images. This automation of the process reduces the time complexity and increases the diagnosis confidence. Our algorithm consists of analysis and diagnosis sections, and detects regions of lung tumor based on image processing techniques and medical knowledge. We have applied our algorithm to 450 patients data for mass screening. The results show that our algorithm detects lung cancer candidates successfully.

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.

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.

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.

Detection algorithm of lung cancer candidate nodules on multislice CT images

Recently, multi-slice helical CT technology was developed. Unlike the conventional helical CT, we can obtain CT images of two or more slices with 1 time of scan. Therefore, we can get many pictures with a clear contrast images and thin slice images in one time of scanning. The nodule is expected to be picture more clearly, and it is expected an high diagnostic ability of screening by the expert physicians. Multi-slice CT is z-axial high-contrast resolution, but the number of images is 10 times the single-slice helical CT. Therefore, the development of a diagnosis support system is expected to diagnose these images. We have developed a computer aided diagnosis (CAD) system to detect the lung cancer from multi-slice CT images. Using the conventional algorithm, it was difficult to detect the ground glass shadow and the nodules in contact with the blood vessel. The purpose of this study is to develop a detection algorithm using the 3-D filter by orientation map of gradient vectors and the 3-D distance transformation.

Computer Assisted Lung Cancer Diagnosis Based on Helical Images

In this paper, we describe a computer assisted automatic diagnosis system of lung cancer that detects tumor candidates in its early stage from the helical CT images. This automation of the process reduces the time complexity and increases the diagnosis confidence. Our algorithm consists of analysis part and diagnosis part. In the analysis part, we extract the lung regions and the pulmonary blood vessels regions and analyze the features of these regions using image processing technique. In the diagnosis part, we define diagnosis rules based on these features, and we detect the tumor candidates using these rules. We apply our algorithm to 224 patients data of mass screening. These results show that our algorithm detects lung cancer candidates successfully.

A CAD system for lung cancer based on CT image

Abstract This paper describes a computer-aided detection system and a computer-aided malignant and benign classification system in lung cancer screening using 3D CT image. These systems and techniques are examined effectively by evaluation of the result of clinical tests and the comparison of the receiver operating characteristic (ROC) curves.