FB2 Signal and Image Processing I
Time : 13:30~15:00
Room : Crystal
Chair : Prof.Hyoungseop Kim (Kyushu Institute of Tecnology, Japan)
13:30~13:45        FB2-1
Enhancement of Bone Metastasis from CT Images Based on Salient Region Feature Registration

Suguru Sato, Hyoungseop Kim(Kyusyu Institute of Technology, Japan)

In recent years, the development of the computer-aided diagnosis (CAD) systems to support radiologist is attracting attention in medical research field. One of them is temporal subtraction technique. It is a technique to generate images emphasizing temporal changes in lesions by performing a differential operation between current and previous image of the same subject. In this paper, we propose an image registration method for image registration of current and previous image, to generate temporal subtraction images from CT images and enhanced bone metastasis region. The proposed registration m
13:45~14:00        FB2-2
Detection of Abnormal Shadows on Temporal Subtraction Images Based on Multi-phase CNN

Mitsuaki Nagao, Hyoungseop Kim(Kyushu Institute of Technology, Japan)

Recently, lung cancer is a leading cause of death both in Japan and worldwide. Detection of cancer region in CT images is the most important task to early detection and early treatment. We have designed and developed a framework combining machine learning based on multi-phase CNN and temporal subtraction techniques based on non-rigid image registration algorithm. We performed our proposed technique to 25 thoracic MDCT sets and obtained true positive rates of 93.55%, false positive rates of 10.93 /case.
14:00~14:15        FB2-3
Detection of Phalange Region Based on U-Net

Kazuhiro Hatano, Hyoungseop Kim(Kyushu Institute of Technology, Japan)

Visual screening using Computed Radiography (CR) images is effective for diagnosis of osteoporosis, but there are problems of increasing the burden on doctors. In order to solve this problem, we propose segmentation methods of the phalange region from the phalangeal CR images as a preprocessing of classification of osteoporosis. We construct a segmentation model using U-Net, which is a type of deep convolution neural network. The proposed method was applied to input images generated from CR images of 101 patients with both hands, and the result was 0.914 in IoU.
14:15~14:30        FB2-4
Swallowing Motion Analyzing from Dental MR Imaging Based on AKAZE and Particle Filter Algorithm

Kenta Suetani, Hyoungseop Kim(Kyushu Institute of Technology, Japan)

In recent years, dysphagia is problem among elderly people. Therefore, it is necessary to accurately evaluate swallowing function in order to prevent swallowing disorder beforehand or to detect it early. And it is considered that evaluation of swallowing function using Magnetic Resonance Imaging (MRI) is useful. In order to accurately analyzing of the swallowing motion using a computer aided diagnosis (CAD) system on MR imaging, automatic extraction of the esophagus region, which is a region of interest by the image analysis method, is required. Extraction of the spinal region is required as a
14:30~14:45        FB2-5
Extraction of Median Plane from Facial 3D Point Cloud Images Based on ICP Algorithm

Shinji Yamada, Hyoungseop Kim(Kyushu Institute of Technology, Japan)

Cleft lip is a kind of congenital facial morphological abnormality. In the clinical field of cleft lip, it is necessary to analyze symmetric shape. However, there is no method to analyze the cleft lip technique based on symmetrical viewpoints. On the other hand, in our previous method to find a symmetric axis using a 2D image, since the middle line is extracted only from the front view of the face moire image. There was a problem that low accuracy was obtained by slight rotation of the face and it was not possible to consider 3D information. In this paper, we propose a method to extract the me

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