The patients of osteoporosis comprised about 11 million people in Japan and it is one of the problems the aging society
has. In order to prevent the osteoporosis, it is necessary to do early detection and treatment. The development of Multislice
CT technology made it possible to perform the three dimensional (3-D) image analysis with higher body axis
resolution and shorter scan time. The 3-D image analysis using multi-slice CT images of thoracic vertebra can be used as
a support to diagnose osteoporosis and at the same time can be used for lung cancer screening which may lead to its early
detection. We develop an automatic extraction algorithm of vertebra, and the analysis algorithm of the vertebral body
using shape analysis and a bone density measurement for the computer aided diagnosis of osteoporosis.
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, 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.
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, 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 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.
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.
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.
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.
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.
In this paper we present the effect of the realistically- shaped head model on MEG inverse problem by using head model which is made up of triangle elements. Among many suggested methods until now, the head model most widely used is the sphere model. However the shape of human head is different from sphere and the conductivity of the head is not uniform. As basic study for accurate estimation, we present the effect of the realistically-shaped head model on MEG inverse problem by using head model which is made up of triangle elements. Then, we perform the estimation on the extract cortex by means of the Multiple Signal Classification (MUSIC) method.
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