Medical imaging fusion applications: An overview

Abstract

Computer aided fusion of multi modality medical images provides a very promising diagnostic tool with numerous clinical applications. The objective of this paper is to present an overview of medical imaging fusion techniques with an emphasis on the use of neural network algorithms. Case studies derived from oncology (data level fusion), microscopy and ultrasound imaging (feature level and decision level fusion), and lesion placement in pallidotomy (data level fusion) are presented. It is anticipated that these tools will help the physician towards a more realistic and quantitative, assessment of disease.

Medical image fusion: A survey of the state of the art

“…[324] R. Teodorescu, C. Cernazanu-Glavan, V. Cretu, D. Racoceanu, The use of the medical ontology for a semantic-based fusion system in biomedical informatics application to Alzheimer's disease, in: [19], tissue classification [20], brain diagnosis [20], classifier fusion [21], breast cancer tumor detection [21,22], delineation & recognition of anatomical brain object [18] and medical image retrieval [23,24,25] [36], classification [36], fusion [36,19,27,37,38,27,39,40,41,42,43], micro-calcification diagnosis [19], breast cancer detection [38,44,45], medical diagnosis [27,28,42] [47,48,49,50], cancer treatment [51], image segmentation and integration [51,52], maximization mutual information [53], deep brain stimulation [54], brain tumor segmentation [55], image retrieval [56,57], spatial weighted entropy [56], feature fusion…”

“…This makes the neural network attractive to image fusion as the nature of variability between the images is subjected to change every time a new modality is used. The ability to train the neural network to adopt to these changes enable several applications for medical image fusion such as solving the problems of feature generation [36], classification [36], data fusion [36,19,27], image fusion [37,38,27,39,40,41,42,43], micro-calcification diagnosis [19], breast cancer detection [38,44,45], medical diagnosis [27,28,42], cancer diagnosis [46], natural computing methods [87] and classifier fusion [45].…”

A Survey on Multimodal Medical Image Fusion

“…This makes the neural network attractive to image fusion as the nature of variability between the images is subjected to change every time a new modality is used. The ability to train the neural network to adopt to these changes enable several applications for medical image fusion such as solving the problems of feature generation [36], classification [36], data fusion [36,19,27], image fusion [37,38,27,39,40,41,42,43], micro-calcification diagnosis [19], breast cancer detection [38,44,45], medical diagnosis [27,28,42], cancer diagnosis [46], natural computing methods [87] and classifier fusion [45]. Although ANN offers generality in terms of having the ability to apply the concept of training, the robustness of ANN methods is limited by the quality of the training data and the accuracy of convergence of the training algorithm.…”

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

“…Through image fusion, it is possible to integrate and present the information from two or more imaging modalities in a more effective way. Image fusion finds applications in Oncology, Neurology, Cardiology and others [2,3]. Fusion of CT and MR images is used to improve lesion delineation for Radiation therapy planning, prostate seed implant quality analysis [1], and planning the correct surgical procedure in computer-assisted navigated neurosurgery of temporal bone tumors [4], and orbital tumors [5].…”