http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Automatic Segmentation of Phalanges Regions on MR Images Based on MSGVF Snakes
Koji SHIGEYOSHI,Seiichi MURAKAMI,Hyoungseop KIM,Joo Kooi TAN,Seiji ISHIKAWA 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
In recent years, medical imaging is important in medical diagnosis for early detection of lesions. However, a large number of images increases the stress to the radiologist. Therefore, CAD (Computer Aided Diagnosis) system is expected to reduce such burden. In diagnostic imaging of phalanges, X-ray photographs, CT (Computed Tomography) are used to evaluate the value of phalanges destruction. Whereby, the MRI (Magnetic Resonance Imaging) that is used mainly to diagnose the lesion in the soft tissue is more effective in a certain case, which is one of the important CAD systems to develop. However, studies on CAD system using MR images are not as much as the studies using CT. In this paper, we propose an automatic segmentation algorithm of phalanges regions on MR images. Although it has three dimensional information, this is the method for two dimensional algorithm. In other words, we propose for each slice of MR Images. Firstly, phalanges regions in MR images are segmented for coarse regions mainly by watershed algorithm. Next, the segmented results from the previous phase are set as the initial contour. Ultimately, the accurate segmentation of the phalanges on MR images are acquired based on MSGVF snakes.
Denoising on Low-Dose CT Image Using Deep CNN
Yuta Sadamatsu,Seiichi Murakami,Guangxu Li,Tohru Kamiya 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Computed Tomography (CT) scans are widely used in Japan, and they contribute to public health. On the other hand, there is also a risk of radiation exposure. To solve this problem, attempts are being made to reduce the radiation dose during imaging. However, reducing the radiation dose causes noise and degrades image quality. In this paper, we propose an image analysis method that efficiently removes noise by changing the activation function of Deep Convolutional Neural Network (Deep CNN). Experimental tests using full-body slice CT images of pigs and phantom CT images of lungs with Poisson noise show that the proposed method is helpful by comparing them with normal-dose CT images and evaluating image quality using peak signal-to-noise ratio (PSNR).
Segmentation Method for Phalanges in CR Image by Use of DCT
Yoshimichi Hozu,Seiichi Murakami,Hyoungseop Kim,Joo Kooi Tan,Seiji Ishikawa,Takatoshi Aoki 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
In this paper, we propose a CAD (Computer aided diagnosis) system to analyze the RA (rheumatoid arthritis) and osteoporosis by using image processing techniques from the CR images. To analyze the RA, we develop a segmentation method for phalanges in CR Image by use of DCT (Discrete Cosine Transform) for detection of temporal change. The temporal change is detected using the difference image between previous image and current one. The DCT is performed to emphasize the edge of the difference image. Finally, the phalanges are extracted by performing Snakes. The primary objective of this study is to segment phalanges by making temporal subtraction images. We apply our proposed technique to eight cases of CR images and satisfactory segmentation results are achieved. A new index that diagnoses the progress level of the disease of phalanges can be offered as a second opinion.
Registration of Phalange Region from CR Images Based on Genetic Algorithm
Kohei KAWAGOE,Seiichi MURAKAMI,Huimin LU,Joo Kooi TAN,Hyoungseop KIM,Takatoshi AOKI 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
In Japan, the number of patients with osteoporosis and rheumatoid arthritis is increasing. Image diagnosis using CR images is effective for osteoporosis and rheumatoid arthritis. Development of a CAD system is important for reducing burdens on doctors. In this paper, we propose an automatic registration algorithm in the CAD system. In the proposed method, the genetic algorithm is used to register bone regions between identical parts of the same subject with different time series. In the experiment, the proposed method is applied to 176 bone area, and 98.14 % of TPR, 1.85 % of FPR are obtained respectively. Even when the area difference is used as the fitness of the genetic algorithm, it has cross-correlation and positioning accuracy equivalent to mutual information.
Detection of Phalange Region Based on U-Net
Kazuhiro HATANO,Seiichi MURAKAMI,Huimin LU,Joo Kooi TAN,Hyoungseop KIM,Takatoshi AOKI 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
Osteoporosis is one of the famous bone diseases. It is a major cause of deteriorating the quality of life, and early detection and early treatment are becoming socially important. Visual screening using Computed Radiography (CR) images is effective for diagnosis of osteoporosis, but there are problems of increasing the burden on doctors, variation in diagnostic results due to differences in experiences of doctors, and undetected lesions. In order to solve this problem, we are working on a computer-aided diagnosis (CAD) system for osteoporosis. In this paper, we propose segmentation methods of the phalange region from the phalangeal CR images as a preprocessing of classification of osteoporosis. In the proposed method, we construct a segmentation model using U-Net, which is a type of deep convolution neural network (DCNN). The proposed method was applied to input images generated from CR images of 101 patients with both hands, and evaluated using the Intersection over Union (IoU) values. The result was 0.914 in IoU.
Automatic Segmentation of Finger Bone Regions from CR Images Using Improved DeepLabv3+
Hikaru Ono,Seiichi Murakami,Tohru Kamiya,Takatoshi Aoki 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and treatment of these bone diseases are important because they significantly interfere with the quality of life (QOL) as the symptoms progress. Visual screening based on CR is used as a diagnosing tool for bone diseases. However, imaging diagnosis is subjective and lacks objectivity, and there is a possibility that lesions may be overlooked. In addition, it is difficult to find out subtle changes from images, increasing the workload for doctors. To solve these problems, there is a need to develop a computer aided diagnosis (CAD) system that can quantitatively diagnose bone diseases. We propose a method for automatic extraction of phalange regions for the CAD system to diagnose these diseases. The proposed method can extract the phalanges with high accuracy by using the improved DeepLabv3+. In this paper, we apply the proposed method to 101 cases of CR images and mIoU of 0.949 was obtained.
Automatic Segmentation of Phalanges Regions on CR Images Based on MSGVF Snakes
Shota KAJIHARA,Seiichi MURAKAMI,Hyoungseop KIM,Joo Kooi TAN 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
Rheumatoid arthritis and osteoporosis are two common orthopedic diseases. Rheumatoid arthritis is a disease that inflammation occurs in the joint, which always causes the joints are able to move freely. Osteoporosis is a disease that bone mineral content is reduced and risk of fragility fracture increases. As one of the diagnostic methods, medical imaging by photographed CR equipment has been widely accepted. However, some problems such as mass screening data sets and mis-diagnosis are still remained in visual screening. In order to solve these problems and reduce the burden to physicians, needs of an automatic diagnosis system capable of performing quantitative analysis is anticipated. In this paper, we carry out the development of a segmentation method of phalanges regions from CR images of the hand to perform a quantitative evaluation of rheumatoid arthritis and osteoporosis. The proposed method is carried out crude segmentation of phalanges regions from CR images of the hand, and extracts the detailed phalanges regions by Multi Scale Gradient Vector Flow Snakes (MSGVF) method. In our study, we performed Snakes algorithm to give an initial control points on MSGVF algorithm. We applied our method on three pairs of CR temporal images of phalanges regions, which are called as the previous images and the current images. We got the segmentation results of 5.95 [%] of false-positive rate and 92.9 [%] of true-positive rate.
Classification of Osteoporosis from Phalanges CR Images Based on DCNN
Kazuhiro HATANO,Seiichi MURAKAMI,Huimin LU,Joo Kooi TAN,Hyoungseop KIM,Takatoshi AOKI 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
Osteoporosis is known as a disease of bone. Visual screening using Computed Radiography (CR) images is an effective method for osteoporosis, however, there are many similar diseases that exhibit state of low bone mass. In this paper, we propose an automatic identification method of osteoporosis from phalanges CR images. In the proposed method, we implement a classifier based on Deep Convolutional Neural Network (DCNN), and identify unknown CR images as normal or abnormal. For training and evaluating of CNN, we use pseudo color images. In the experiment, we apply our proposal method to 101 cases and TPR of 64.7 [%] and FPR of 6.51 [%] were obtained.