2019 Proceeding
2019 2nd International Conference on Digital Medicine and Image Processing (DMIP 2019) was successfully held in Shanghai, China during November 13-15, 2019. This volume contains papers presented at the DMIP 2019. DMIP is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience.
Proceeding Downloads
Low Dose Brain CT, Comparative Study with Brain Post Processing Algorithm
Computed tomography (CT) scanners and CT exams increase continuously. The researchers aim to minimize the ionizing radiation dose by introducing new CT protocols, providing diagnostic CT images with a lower radiation dose to patients. However, such ...
Segmentation of Vestibular Schwannoma from Multi-parametric Magnetic Resonance Images using Convolutional Neural Network
- Wei-Kai Lee,
- Chih-Chun Wu,
- Tzu Hsuan Huang,
- Chun-Yi, Lin,
- Cheng-Chia Lee,
- Wen-Yuh Chung,
- Po-Shan Wang,
- Chia-Feng Lu,
- Hsiu-Mei Wu,
- Wan-Yuo Guo,
- Yu-Te Wu
In this study, we aim to automatically segment the Vestibular Schwannoma (VS) from multi-parametric magnetic resonance (MR) images before the Gamma Knife (GK) treatment using the deep learning based Convolutional Neural Network (CNN). 516 VS subjects' ...
Predicting Severity of Autism Spectrum Disorder based on Multi-center Multi-modality Data
In recent years, many researchers have done a lot of research on the qualitative diagnosis of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI). However, the quantitative prediction of ASD severity is clinically more important, ...
Evaluation of Liver Phantom for Testing of the Detectability Multimodal for Hepatocellular Carcinoma
This study aims at developing a reusable, multimodal liver phantom, which applies functional vasculature and displays some pathologies, such as Hepatocellular Carcinoma (HCC). This phantom can be used with different modalities, such as Ultrasonography (...
An Image Segmentation Method Based on Peak-valley Principle and K-means Algorithm
In order to avoid K-means algorithm falling into local optimum solution, this paper proposes an image segmentation method based on peak-valley principle and K-means algorithm and apply it to the Computed Tomography(CT) image. Firstly, based on the ...
On the Radon Transform and Linear Transformations of Images
We present a novel original method for estimating and recovering a general geometric transformation which is applied to an image. Our main tool is the Radon Transform; we develop analysis to address the behavior of this transform under a Linear ...
Solar Cell Defect Recognition based on Orthogonal Learning Strategy
The quality of silicon wafers is an important factor restricting the efficiency and service life of photovoltaic power generation. In order to inspect the quality of silicon wafers, a defect recognition method based on orthogonal learning strategy is ...
Ultrasound Tongue Image Classification using Transfer Learning
The ultrasound image of the tongue consists of high-level speckle noise, and efficient approach to interpret the image sequences is desired. Automatic ultrasound tongue image classification is of great interest for the clinical linguists, as hand ...
A Lightweight Channel-spatial Attention Network for Real-time Image De-raining
Image de-raining aims to eliminate rain streaks captured by outdoor equipment such as video surveillance, remote sensor and automatic pilot. Recently, a de-raining method called non-locally enhanced encoder-decoder network (NLEDN) has achieved ...
Towards Tomography with Random Orientation
We consider the two-dimensional parallel beam Tomography problem in which both the object being imaged and the projection directions are unknown. Specifically: Given unsorted set of Radon projections that correspond to angles φj=0°, 1°, ..., 179°. Our ...