Andere Kunden interessierten sich auch für
![Advanced Machine Vision Paradigms for Medical Image Analysis]( "Advanced Machine Vision Paradigms for Medical Image Analysis")
Advanced Machine Vision Paradigms for Medical Image Analysis100,99 €![An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom]( "An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom")
An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom27,99 €![An Overview of Deep Learning in Medical Imaging Focusing on MRI images]( "An Overview of Deep Learning in Medical Imaging Focusing on MRI images")
An Overview of Deep Learning in Medical Imaging Focusing on MRI images29,99 €![Accurate detection of vowel onset points in the mobile environment]( "Accurate detection of vowel onset points in the mobile environment")
Accurate detection of vowel onset points in the mobile environment27,99 €![Novel Disease Prediction System Using Hybrid Deep Learning Techniques]( "Novel Disease Prediction System Using Hybrid Deep Learning Techniques")
Novel Disease Prediction System Using Hybrid Deep Learning Techniques53,99 €![Lung Tumor Detection and Classification]( "Lung Tumor Detection and Classification")
Lung Tumor Detection and Classification27,99 €![MRI Image Segmentation for Detection of Brain Tumors]( "MRI Image Segmentation for Detection of Brain Tumors")
MRI Image Segmentation for Detection of Brain Tumors29,99 €
The main objective of Hybrid Multimodal Medical Image Fusion (HMMIF) technique is to preserve salient image features and detailed information of multiple source images to produce a visual enhanced single fused image. Several multimodal medical image hybrid fusion techniques have been developed for pathological studies such as neurocysticercosis, degenerative and neoplastic diseases. In literature, the commonly reported medical image fusion modalities are CT, MRI, SPECT and PET. Each modality has unique features in clinical diagnosis; at the same time, these modalities have challenges in pathology. In this work, five different domains based multimodal medical image hybrid fusion techniques have been developed for various MRI-SPECT, MRI-PET and MRI-CT medical image fusion applications.