A5083154285- Medical Image Segmentation Techniques
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging and Analysis
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Brain Tumor Detection and Classification
- 3D Shape Modeling and Analysis
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
- Advanced Image Processing Techniques
- Medical Imaging Techniques and Applications
- Computer Graphics and Visualization Techniques
- Artificial Intelligence in Healthcare
- Image and Signal Denoising Methods
- Advanced X-ray and CT Imaging
- Image Processing Techniques and Applications
- COVID-19 diagnosis using AI
- Ultrasound and Cavitation Phenomena
- Digital Imaging for Blood Diseases
- Retinal Imaging and Analysis
- Advanced Image Fusion Techniques
- Liver Disease Diagnosis and Treatment
- Fluid Dynamics and Mixing
- Lung Cancer Diagnosis and Treatment
Shahed University
2015-2024
Ritsumeikan University
2009-2015
Central South University of Forestry and Technology
2015
Central South University
2015
University of Tehran
2009-2012
Seikei University
2012
In this paper, we present an algorithm to segment the liver in low-contrast CT images. As first step of our algorithm, define a search range for boundary. Then, EM is utilized estimate parameters 'Gaussian Mixture' model that conforms intensity distribution liver. Using statistical distribution, introduce new thresholding technique classify image pixels. We assign distance feature vectors each pixel and by K-means clustering scheme. This initial boundary conditioned Fourier transform....
Extraction of blood vessels the organ is a challenging task in area medical image processing. It really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty complicated structure and its large variations that make them hard recognize. In this paper, we present deep artificial neural network architecture automatically segment hepatic from computed tomography (CT) image. We proposed novel (DNN) for CT volume, which consists three...
Diagnosis techniques based on medical image modalities have higher sensitivities compared to conventional RT-PCT tests. We propose two methods for diagnosing COVID-19 disease using X-ray images and differentiating it from viral pneumonia. The diagnosis section is deep neural networks, the discriminating uses an retrieval approach. Both units were trained by healthy, pneumonia, images. In patients, maximum intensity projection of lung CT visualized a physician, Involvement Score calculated....
In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in shapes different organs. This paper focuses on construction a SSM liver and its application to computer-assisted diagnosis cirrhosis. We prove potential SSMs classification normal cirrhotic livers. constructing liver, we first normalize volume data followed by using principal component analysis. The coefficients are as indicators pathology. effectiveness constructed evaluated...
Statistical shape model (SSM) is to the variation of an object. In this paper, we propose efficient representation method and a new 2D-PCA based statistical modeling. our proposed method, used radii these surface points as feature instead their coordinates, represented by 2D matrices. We then apply construct with generalization even from fewer samples.
We developed a Computer Assisted Surgery system which prepared virtual environment for physician to interact with the liver and decide on therapy planning.It was composed of three modules: segmentation, vessel extraction, simulator.We proposed semi-automatic method segment liver.Hepatic veins, portal hepatic arteries were extracted from multi-phase CT datasets.The simulator visualized segmented objects provided scalpel cut liver.Initially, transparent view shown that revealed location...