Fusion Strategy for Improving Medical Image Segmentation

Image Fusion For Medical Image Retrieval

A Fully Automatic Scheme for Medical Image Segmentation with Wavelet Based Image Fusion

Medical image segmentation has become an essential technique in clinical and researchoriented applications. Because manual segmentation methods are tedious, and semi automatic segmentation lacks the flexibility, fully-automatic methods have become the preferred type of medical image segmentation. This work proposes a robust fully automatic segmentation scheme based on the modified contouring te...

A hidden Markov Model for image fusion and their joint segmentation in medical image computing

In this work we propose a Bayesian framework for fully automated image fusion and their joint segmentation. More specifically, we consider the case where we have observed images of the same object through different image processes or through different spectral bands. The objective of this work is then to propose a coherent approach to combine these data sets and obtain a segmented image which c...

Partial Differential Equations applied to Medical Image ‎Segmentation

‎This paper presents an application of partial differential equations(PDEs) for the segmentation of abdominal and thoracic aortic in CTA datasets. An important challenge in reliably detecting aortic is the need to overcome problems associated with intensity inhomogeneities. Level sets are part of an important class of methods that utilize partial differential equations (PDEs) and have been exte...

Medical Image Segmentation

Image data is of immense practical importance in medical informatics. Medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), Ultrasound, and X-Ray, in standard DICOM formats are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information in EHR clinical management systems. Research efforts have been devoted to...