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Image Registration of Vertebral Region from CT Images Based on Salient Region Feature
Suguru SATO,Huimin LU,Joo Kooi TAN,Hyoungseop KIM,Seiichi MURAKAMI,Midori UENO,Takashi TERASAWA,Takatoshi AOKI 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
In recent years, the development of the computer-aided diagnosis (CAD) systems to support radiologist is attracting attention in medical research field. Temporal subtraction technique, which is one of CAD, 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 method is composed into three main steps: i) segmentation of the region of interest (ROI) using graph cut, ii) use global image matching to select pairs from previous and current image, and iii) final image matching based on salient region feature. We perform our proposed method to synthesis and satisfactory registration experiments. The rotated synthesis image give TP 100.0[%] and FP 12.16[%]. The synthesis image obtained by applying a Gaussian filter give TP 70.40[%] and FP 0.00[%]. The synthesis image obtained by adding artificial pseudo lesion region give TP 99.45[%] and FP 17.89[%]. The synthesis image obtained by adding random noise of 5[%], which gave TP 83.05[%] and FP 16.95[%].
Enhancement of Bone Metastasis from CT Images Based on Salient Region Feature Registration
Suguru Sato,Huimin Lu,Hyoungseop Kim,Seiichi Murakami,Midori Ueno,Takashi Terasawa,Takatoshi Aoki 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
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 method is composed into three main steps: i) automatic segmentation of the region of interest (ROI) using position information of the spine based on biology, ii) use global image matching to select pairs from previous and current image, and iii) final image matching based on salient region feature. We perform registration technique on synthetic data and confirm usefulness of the proposed method. Furthermore, radiologist conduct comparative experiments without and with temporal subtraction images created by proposed method. As a result, they show high reading performance by using temporal subtraction images.
Segmentation of Bone Metastasis in CT Images Based on Modified HED
Yuchan Song,Huimin, Lu,Hyoungseop Kim,Seiichi Murakami,Midori Ueno,Takashi Terasawa,Takatoshi Aoki 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
Segmentation of the bone metastasis area in medical images can reduce the workload for diagnosis and treatment. However, there are various shapes and sizes of bone metastasis also affected by noise. As a result, it is difficult to segment using classical segmentation methods. In this paper, we propose a convolutional neural network model-based segmentation method. The proposed method easily predicts the contour and location of the lesion area using side connection and modified network. In this study, we modified again the modified HED network to match the characteristics of bone metastasis. The experimental results using the proposed method for segmenting bone metastasis in the lesion area has 79.8[%] of TP rate and 69.2[%] of IOU rate.