As multi-slice CT develops, there are great expectations for an automatic and computer-support diagnoses. This
research is on bronchial area which is composed of the bronchial wall regions and the air regions in the internal bronchial
tube. Since to diagnose this is difficult, support diagnosis using CT images is desired. The thickness of bronchial wall
changes as the airway of early lung cancer, bronchial asthma and the bronchial enhancing syndrome and others change
into a malignant state. These changes are detected and the thickness of bronchial wall becomes important information. In
this research, the extraction accuracy of the algorithm for bronchial wall evaluation is good.
Automated distinction of medical images is an important preprocessing in Computer-Aided Diagnosis (CAD) systems.
The CAD systems have been developed using medical image sets with specific scan conditions and body parts. However,
varied examinations are performed in medical sites. The specification of the examination is contained into DICOM
textual meta information. Most DICOM textual meta information can be considered reliable, however the body part
information cannot always be considered reliable. In this paper, we describe an automated distinction of DICOM images
as a preprocessing for lung cancer CAD system. Our approach uses DICOM textual meta information and low cost
image processing. Firstly, the textual meta information such as scan conditions of DICOM image is distinguished.
Secondly, the DICOM image is set to distinguish the body parts which are identified by image processing. The
identification of body parts is based on anatomical structure which is represented by features of three regions, body
tissue, bone, and air. The method is effective to the practical use of lung cancer CAD system in medical sites.
The five year survival rate of the lung cancer is low with about twenty-five percent. In addition it is an obstinate lung
cancer wherein three out of four people die within five years. Then, the early stage detection and treatment of the lung
cancer are important. Recently, we can obtain CT and PET image at the same time because PET/CT device has been
developed. PET/CT is possible for a highly accurate cancer diagnosis because it analyzes quantitative shape information
from CT image and FDG distribution from PET image. However, neither benign-malignant classification nor staging
intended for lung cancer have been established still enough by using PET/CT images. In this study, we detect lung
nodules based on internal organs extracted from CT image, and we also develop algorithm which classifies benignmalignant
and metastatic or non metastatic lung cancer using lung structure and FDG distribution(one and two hour after
administering FDG). We apply the algorithm to 59 PET/CT images (malignant 43 cases [Ad:31, Sq:9, sm:3], benign 16
cases) and show the effectiveness of this algorithm.
Emphysema patients have the tendency to increase due to aging and smoking. Emphysematous disease destroys
alveolus and to repair is impossible, thus early detection is essential. CT value of lung tissue decreases due to the
destruction of lung structure. This CT value becomes lower than the normal lung- low density absorption region or
referred to as Low Attenuation Area (LAA). So far, the conventional way of extracting LAA by simple thresholding has
been proposed. However, the CT value of CT image fluctuates due to the measurement conditions, with various bias
components such as inspiration, expiration and congestion. It is therefore necessary to consider these bias components in
the extraction of LAA. We removed these bias components and we proposed LAA extraction algorithm. This algorithm
has been applied to the phantom image. Then, by using the low dose CT(normal: 30 cases, obstructive lung disease: 26
cases), we extracted early stage LAA and quantitatively analyzed lung lobes using lung structure.
With thin and thick section Multi-slice CT images at lung cancer screening, we have statistically and quantitatively
shown and evaluated the diagnostic capabilities of these slice thicknesses on physicians' pulmonary nodule diagnosis. To
comparatively evaluate the 2 mm and 10 mm slice thicknesses, MSCT images of 360 people were read by six physicians.
The reading criteria consisted of nodule for further examination (NFE), nodule for no further examination (NNFE) and
no abnormality (NA) case. For reading results evaluation; firstly, cross-tabulation was carried out to roughly analyze the
diagnoses based on whole lung field and each lung lobes. Secondly, from semi-automated extraction result of the nodule,
detailed quantitative analysis was carried out to determine the diagnostic capabilities of two slice thicknesses. Finally,
using the reading results of 2 mm thick image as the gold standard, the diagnostic capabilities were analyzed through the
features and locations of pulmonary nodules. The study revealed that both slice thicknesses can depict lung cancer. Thin
section may not be effective to diagnose nodules of ≤3 mm in size and nodules of ≤ 5mm in size for thick section.
Though thick section is less tiring for reading physicians, it is not good at depicting nodules located at the border of lung
upper lobe and which have a pixel size distance of ≤5 from the chest wall. The information presented may serve as a
useful reference to determine in which particular pulmonary nodule condition the two slice thicknesses can be effectively
used for early detection of lung cancer.
Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was
destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative
algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose
thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as
emphysematous lesion candidates. Applying the algorithm to thoracic 3-D CT images and then by follow-up 3-D CT
images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the
emphysematous lesions distribution and their evolution in time interval changes.
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 scan. Therefore, we can get many pictures with a clear contrast images and thin
slice images in one time of scanning. The purpose of this presentation is to evaluate the proposed automatic extraction
bronchus and pulmonary vein and artery on multi-slice CT images. The bronchus is extracted by application with region
growing technique and the morphological filters, 3D distance transformation. These results indicate that the proposed
algorithm provides the ability to accurately develop an automatic extraction algorithm of the bronchus on multi-slice CT
images. In this report, we used pulmonary vein and artery marked by the doctor, It aims to discover an amount of the
feature necessary for classifying the pulmonary vein and artery by using the anatomical feature. The classification of the
pulmonary vein and artery is thought necessary information that it is state of tuber benign or malignity judgment. It is
very important to separate the contact part of the lung blood vessel in classifying pulmonary vein and artery. Then, it
aims to discover the feature of the contact part of the lung blood vessel in this report.
Multi-slice CT technology was developed, so, we can get clear contrast images and thin slice images. But doctors need
to diagnosis many image, thus their load increases. Therefore, development of the algorithm that analyses lung internal-organs
is expected. When doctors diagnose lung internal-organs, they understand it. So, detailed analyze of lung internal-organs
is applicant to early detection of a nodule. Especially, analyzing bronchus provides that useful information of
detection of airway disease and classification of the pulmonary vein and artery. In this paper, we describe a method for
automated anatomical labeling algorithm of bronchial branches based on Multi-Slice CT images.
Nowadays, dental CT images play more and more important roles in oral clinical applications. Our research is important
particularly in the field of dentistry. We are using non-dynamic and dynamic CT image for our research. We are creating
our database of bone, blood vessels and muscles of head and neck. This database contains easy case and difficult case of
head and neck's bone, blood vessels and muscle. There are lots of difficult cases in our database. Teeth separation and
condylar process separation is difficult case. External carotid artery has many branches and they are attached with vain
so it is difficult to separate. All muscle threshold value is same and they are attaching with each other so muscle
separation is very difficult. These databases also contain different age's patients. For this reason our database becomes
an important tool for dental students and also important assets for diagnosis. After completion our database we can link
it with other dental application.
KEYWORDS: Human-machine interfaces, Data communications, Image processing, Surgery, Ions, Computed tomography, Bone, Medical imaging, Databases, Picture Archiving and Communication System
We have developed an anonymization system for DICOM images. It requires consent from the patient to use the DICOM images for research or education. However, providing the DICOM image to the other facilities is not safe because it contains a lot of personal data. Our system is a server that provides anonymization service of DICOM images for users in the facility. The distinctive features of the system are, input interface, flexible anonymization policy, and automatic body part identification. In the first feature, we can use the anonymization service on the existing DICOM workstations. In the second feature, we can select a best policy fitting for the Protection of personal data that is ruled by each medical facility. In the third feature, we can identify the body parts that are included in the input image set, even if the set lacks the body part tag in DICOM header. We installed the system for the first time to a hospital in December 2005. Currently, the system is working in other four facilities. In this paper we describe the system and how it works.
Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to 100 thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.
Multi-slice helical CT technology has been developed. Unlike the conventional helical CT, we can obtain CT images of two or more slices in 1 time scan. Therefore, we can get many images with a clear contrast and thin slice images in one time of scanning. The purpose of this presentation is to evaluate the proposed automatic extraction bronchus and pulmonary vein and artery on multi-slice CT images. The bronchus is extracted by application with region growing technique and the morphological filters, 3D distance transformation. These results indicate that the proposed algorithm provides the ability to accurately develop an automatic extraction algorithm of the bronchus on multi-slice CT images. In this report, we used pulmonary vein and artery marked by the doctor, It aims to discover an amount of the feature necessary for classifying the pulmonary vein and artery by using the anatomical feature. The classification of the pulmonary vein and artery is thought to be a necessary information for tumor's benign or malignity judgment. In this report, the amount of the feature in which the flow of the automation is based is analyzed by using three dimension images of pulmonary vein and artery and bronchus obtained by the specialized physician's marking.
Multi-slice CT technology was developed, so, we can get clear contrast images and thin slice images. But doctors need to diagnosis many image, thus their load increases. Therefore, development of the algorithm that analyses lung internal-organs is expected. When doctors diagnose lung internal-organs, they understand it. So, detailed analyze of lung internal-organs is applicant to early detection of a nodule. Especially, analyzing bronchus provides that useful information of detection of airway disease and classification of the pulmonary vein and artery. In this paper, we describe a method for automated anatomical labeling algorithm of bronchial branches based on Multi-Slice CT images.
This paper presents a method for detecting suspicious nodules based on successive low-dose helical CT images. The method uses both initial and follow-up images to improve nodule detection performance. The basic idea of the detection is to register nodule images measured at different time and to assess the changes in size, shape, and density of the nodule. Since there are several variations of nodule changes, such as stable, shrinking, expansion in size, disappearance, appearance, and separation, a coarse-to-fine registration technique was adopted to deal with large nodule deformation. Especially, the fine registration is performed by excluding nodule regions and using nodule surroundings to avoid effects of nodule deformations in alignment task. In a preliminary experiment, the method was applied to ten cases with successive scans. From visual inspection, the corresponding results between initial and follow-up images were acceptable in clinical use. More researches using a large data set will be required. Still, we believe that the method has the potential of detecting suspicious nodules for use in a computer-aide diagnosis system.
Our group developed the computer aided diagnosis (CAD) system for lung cancer in 1996, and has been used in clinical field since 1997. From this CAD system (conventional system), we discovered problem and we attempted to solve the problem by using our proposed algorithm. The proposed algorithm succeeded in the improvement of the following three problems of the conventional system. (1) Weak extraction algorithm of region of interest (ROI) with noise, (2) Poor knowledge of chest structure, and (3) diagnostic processing for nodule of limited size. In this paper, the algorithm that solves problem (2) and (3) is described. We evaluated the proposed algorithm, which was applied to the following four databases. (A) Lung cancer database, (B) detailed examination database, (C) a large-scale screening database by 10mm-thickness images reconstructed from single-slice CT scan, and (D) a large-scale screening database by 10mm-thickness images reconstructed from multi-slice CT scan. The proposed method obtained the following successful results: Lung cancer database 95.7% TP and detailed examination 94.8% TP. For the large-scale screening database, we evaluated each examination process from physicians’ reading to cancer decision. The extraction rate of proposed algorithm improved as the examinations proceed. Two false positive results were obtained. False positive 1 (6.8-9.2 shadows/case) needed for a detailed examination and the object of false positive 2 (2.6-4.0 shadows/case) was an abnormal shadow.
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.
Micro CT system is developed for lung function analysis at a high resolution of the micrometer order (up to 5μm in spatial resolution). This system reveals the lung distal structures such as interlobular septa, terminal bronchiole, respiratory bronchiole, alveolar duct, and alveolus. In order to visualize lung 3-D microstructures using micro CT images and to analyze them, this research presents a computerized approach. This approach is applied for to micro CT images of human lung tissue specimens that were obtained by surgical excision and were kept in the state of the inflated fixed lung. This report states a wall area such as bronchus wall and alveolus wall about the extraction technique by using the surface thinning process to analyze the lung microstructures from micro CT images measured by the new-model micro CT system.
Recently, the multi-slice CT for a lung cancer screening has 10 times as high resolution as the conventional helical CT. Small nodules can be found in this CT image. The diagnostic ability of expert physicians will improve by using this CT images. We get the CT image of 2mm thickness and 10 mm thickness from each subject. In this paper, we evaluated about the reading test for the lung cancer by expert physicians, using two kinds of images.
Recently, the development of multi-row multi-slice CT scanner proves precise measure of whole lung area in short time period. The Ct scanner improves spatial resolution along z-axis and time resolution. Therefore, this CT image is effective for diagnosis of lung cancer as well as the other lung lesions, and leads the early detection. For clinical decision, lung lesion diagnosis requires the lung area detection. Pulmonary fissure part is one of important organs to identify lung area. This paper presents an algorithm of accuracy and automatic pulmonary extraction fissure based on the search area specification of pulmonary fissure, and accuracy pulmonary fissure extraction effectiveness of low-dose and high resolution multi-slice CT image. The usefulness of the proposed algorithm is demonstrated by using twenty clinical data sets.
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.
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.
Recently, the development of multi-row multi-slice CT scanner proves precise measure of whole lung area in short time period. The CT scanner improves spatial resolution along z-axis and time resolution. Therefore, this CT image is effective for diagnosis of lung cancer as well as the other lung lesion, and leads the early detection. The development of a diagnosis support system is expected to diagnose these images.
So far, we have developed a computer-aided diagnosis (CAD) system to automatically detect suspicious regions based on helical CT image. However, the algorithm isn't enough in multi-slice CT images because of two-dimensional algorithm and un-recognizing of the chest structure. This paper presents an algorithm of nodules detection using the three-dimensional (3-D) algorithm and recognizing of the chest structure based on multi-slice CT images, and we show the validity of detection algorithm of isolated nodules using 286 data sets.
Chest CT images obtained by CT scanner have drawn a great interest in suspicious region detection. However, mass screening based on CT images leads a considerable number of images to be diagnosed. CAD system for lung cancer that detects tumor candidates at early stage from CT images has developed. In July 1997, clinical trial using first version of the CAD system in Anti-Lung Cancer Association (National Cancer Center Hospital and National Cancer Hospital East) started. As following stage, clinical trial using second version of the CAD system that supports comparative reading function started in April 2002. We expect that CAD system reduces diagnosis time and increase the reliability. In this paper, we describe the clinical trial results using the CAD system from July 1997 and the detection results of the CAD system for supporting mass screening in a prospective study. The results show that the CAD system improves diagnostic accuracy and throughput. And we describe the future of CAD system.
Micro CT system is developed for lung function analysis at a high resolution of the micrometer order (up to 5 μm in spatial resolution). This system reveals the lung distal structures such as interlobular septa, terminal bronchiole, respiratory bronchiole, alveolar duct, and alveolus. In order to visualize lung 3-D microstructures using micro CT images and to analyze them, this research presents a computerized approach. In this approach, the following things are performed: (1) extracting lung distal structures from micro CT images, (2) visualizing extracted lung microstructure in three dimensions, and (3) visualizing inside of lung distal area in three dimensions with fly-through. This approach is applied for to micro CT images of human lung tissue specimens that were obtained by surgical excision and were kept in the state of the inflated fixed lung. And this research succeeded in visualization of lung microstructures using micro CT images to reveal the lung distal structures from bronchiole up to alveolus.
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 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.
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.
We have been developed a computer-aided diagnosis (CAD) system in the lung cancer detection from a low-dose single-slice CT scanner. The objective of this study is to solve three problems of the conventional CAD system; application of image obtained by other CT scanner, diagnostic procedure for the ground glass shadow less than 5 mm in diameters, and diagnostic procedure for nodule in contact with blood vessels. We analyzed characteristics between each CT images, and pattern of blood vessels. The structural analysis procedure using three-dimensional data is the newly added process. The diagnostic rules to detect nodule consist of the four classes, which are divided by size and CT value. We applied two lung cancer databases; 55 nodules of TCT-900S and 67 nodules of Asteion. The present result from the former database achieved a sensitivity of 94.5%, the latter database achieved a sensitivity of 90.0%. Most of false negative cases had two cases which are a nodule overlapped by blood vessels and a nodule on mediastinum.
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.
We have been developed a computer-aided diagnosis (CAD) system for the lung cancer detection of early stage from low dose single-slice computed tomography (CT) with 10 mm beam width on chest screening. The objective of this study is to solve three problems of the conventional CAD system; (1) lesion which overlaps blood vessel, (2) lesion in contact with blood vessel and (3) lesion near upper mediastinum. This paper presents a new method to solve problem-1 and problem-2. The blood vessels, which overlap lesions and others in contact with lesion, are eliminated by detecting region of interest (ROI) with accuracy. Detection method of ROIs consists of 3 processes; firstly, streak shadows elimination using linear feature detector filter, secondly, estimation of pulmonary background bias using the intensity histogram and the opening method, and finally, ROI's border detection using laplacian filter. We evaluated the new system by apply it to 155 shadows which need confirmation diagnosis. These cases were selected from clinical test from July 1997 to December 2000 in retrospective study. True positive cases of this algorithm achieved sensitivity 91.0 %. The average of false positive cases was 0.53 per slice.
We have developed a computer assisted automatic detection system for lung cancer that detects tumor candidates at an early stage from helical CT images. In July 1997, we started the comparative field trial using our system prospectively. Chest CT images obtained by helical CT scanner have drawn a great interest in the detection of suspicious regions. However, mass screening based on helical CT images leads to a considerable number of images to be diagnosed. We expect that our system can reduce the time complexity and increase diagnostic confidence. In this paper, we describe the detection results of the system for the nodules of definite diagnosis. We show the clinical test results in a prospective study. The results show that the system can detect lung cancer candidates at an early stage successfully and can be applied to a mass screening. In addition, we describe the necessity of the CAD system having the function which can be compared with the previous CT images.
The objective of our study is to develop a new computer- aided diagnosis (CAD) system to support effectually the comparative reading using serial helical CT images for lung cancer screening without using the film display. The placement of pulmonary shadows between the serial helical CT images is sometimes different to change the size and the shape of lung by inspired air. We analyzed the motion of the pulmonary structure using the serial cases of 17 pairs, which are different in the inspired air. This algorithm consists of the extraction process of region of interest such as the lung, heart and blood vessels region using thresholding and fuzzy c-means method, and the comparison process of each region in serial CT images using template matching. We validated the efficiency of this algorithm by application to image of 60 subjects. The algorithm could compare the slice images correctly in most combinations with respect to physician's point of view. The experimental results of the proposed algorithm indicate that our CAD system without using the film display is useful to increase the efficiency of the mass screening process.
We have developed a computer assisted automatic detection system for lung cancer that detects tumor candidates at an early stage form helical CT images. In July 1997, we started the comparative field trial using our system prospectively. Chest CT images obtained by helical CT scanner have drawn a great interest in the detection of suspicious regions. However, mass screening based on helical CT images leads to a considerable number of images to be diagnosed. We expect that our system can reduce the time complexity and increase diagnostic confidence. In this paper, we describe the detection results of the system for the nodules of definite diagnosis. We show the prospective results and the retrospective results. These results show that the system can detect lung cancer candidates at an early stage successfully and can be applied to a mass screening. In addition, we describe the necessity of the CAD system having the function which can be compared with the previous CT images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.