Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture.

Technology: general issues --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture.

Technology: general issues --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment