scholarly journals Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network

2016 ◽  
Vol 3 (1) ◽  
pp. 17
Author(s):  
Tadashi Kondo ◽  
Junji Ueno ◽  
Shoichiro Takao
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Ct Images ◽  
X Ray

Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

2017 ◽  
Vol 23 (2) ◽  
pp. 271-278
Author(s):  
Shoichiro Takao ◽  
Sayaka Kondo ◽  
Junji Ueno ◽  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Ct Images ◽  
X Ray

Medical image analysis of 3D CT images based on extension of Haralick texture features

2008 ◽  
Vol 32 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Ludvík Tesař ◽  
Akinobu Shimizu ◽  
Daniel Smutek ◽  
Hidefumi Kobatake ◽  
Shigeru Nawano
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Texture Features ◽  
Ct Images ◽  
3D Ct

Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images

2017 ◽  
Vol 23 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Shoichiro Takao ◽  
Sayaka Kondo ◽  
Junji Ueno ◽  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Images ◽  
Mri Brain ◽  
Mri Brain Images

scholarly journals Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

2021 ◽  
Vol 11 (9) ◽  
pp. 4247
Author(s):  
Minh-Trieu Tran ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Image Analysis ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Image Inpainting ◽  
Ct Images ◽  
Task Learning ◽  
Specular Reflections ◽  
Simultaneous Training

Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.

Sign in / Sign up

Export Citation Format

Share Document