785 research outputs found
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Image decomposition is crucial for many image processing tasks, as it allows
to extract salient features from source images. A good image decomposition
method could lead to a better performance, especially in image fusion tasks. We
propose a multi-level image decomposition method based on latent low-rank
representation(LatLRR), which is called MDLatLRR. This decomposition method is
applicable to many image processing fields. In this paper, we focus on the
image fusion task. We develop a novel image fusion framework based on MDLatLRR,
which is used to decompose source images into detail parts(salient features)
and base parts. A nuclear-norm based fusion strategy is used to fuse the detail
parts, and the base parts are fused by an averaging strategy. Compared with
other state-of-the-art fusion methods, the proposed algorithm exhibits better
fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table
CELL PATTERN CLASSIFICATION OF INDIRECT IMMUNOFLUORESCENCE IMAGES
Ph.DDOCTOR OF PHILOSOPH
Multimodal Image Fusion and Its Applications.
Image fusion integrates different modality images to provide comprehensive information of the image content, increasing interpretation capabilities and producing more reliable results. There are several advantages of combining multi-modal images, including improving geometric corrections, complementing data for improved classification, and enhancing features for analysis...etc.
This thesis develops the image fusion idea in the context of two domains: material microscopy and biomedical imaging. The proposed methods include image modeling, image indexing, image segmentation, and image registration. The common theme behind all proposed methods is the use of complementary information from multi-modal images to achieve better registration, feature extraction, and detection performances.
In material microscopy, we propose an anomaly-driven image fusion framework to perform the task of material microscopy image analysis and anomaly detection. This framework is based on a probabilistic model that enables us to index, process and characterize the data with systematic and well-developed statistical tools. In biomedical imaging, we focus on the multi-modal registration problem for functional MRI (fMRI) brain images which improves the performance of brain activation detection.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120701/1/yuhuic_1.pd
Brain Tumor Detection and Segmentation in Multisequence MRI
Tato prĂĄce se zabĂœvĂĄ detekcĂ a segmentacĂ mozkovĂ©ho nĂĄdoru v multisekvenÄnĂch MR obrazech se zamÄĆenĂm na gliomy vysokĂ©ho a nĂzkĂ©ho stupnÄ malignity. Jsou zde pro tento ĂșÄel navrĆŸeny tĆi metody. PrvnĂ metoda se zabĂœvĂĄ detekcĂ prezence ÄĂĄstĂ mozkovĂ©ho nĂĄdoru v axiĂĄlnĂch a koronĂĄrnĂch Ćezech. JednĂĄ se o algoritmus zaloĆŸenĂœ na analĂœze symetrie pĆi rĆŻznĂœch rozliĆĄenĂch obrazu, kterĂœ byl otestovĂĄn na T1, T2, T1C a FLAIR obrazech. DruhĂĄ metoda se zabĂœvĂĄ extrakcĂ oblasti celĂ©ho mozkovĂ©ho nĂĄdoru, zahrnujĂcĂ oblast jĂĄdra tumoru a edĂ©mu, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkovĂœ nĂĄdor z 2D i 3D obrazĆŻ. Je zde opÄt vyuĆŸita analĂœza symetrie, kterĂĄ je nĂĄsledovĂĄna automatickĂœm stanovenĂm intenzitnĂho prahu z nejvĂce asymetrickĂœch ÄĂĄstĂ. TĆetĂ metoda je zaloĆŸena na predikci lokĂĄlnĂ struktury a je schopna segmentovat celou oblast nĂĄdoru, jeho jĂĄdro i jeho aktivnĂ ÄĂĄst. Metoda vyuĆŸĂvĂĄ faktu, ĆŸe vÄtĆĄina lĂ©kaĆskĂœch obrazĆŻ vykazuje vysokou podobnost intenzit sousednĂch pixelĆŻ a silnou korelaci mezi intenzitami v rĆŻznĂœch obrazovĂœch modalitĂĄch. JednĂm ze zpĆŻsobĆŻ, jak s touto korelacĂ pracovat a pouĆŸĂvat ji, je vyuĆŸitĂ lokĂĄlnĂch obrazovĂœch polĂ. PodobnĂĄ korelace existuje takĂ© mezi sousednĂmi pixely v anotaci obrazu. Tento pĆĂznak byl vyuĆŸit v predikci lokĂĄlnĂ struktury pĆi lokĂĄlnĂ anotaci polĂ. Jako klasifikaÄnĂ algoritmus je v tĂ©to metodÄ pouĆŸita konvoluÄnĂ neuronovĂĄ sĂĆ„ vzhledem k jejĂ znĂĄme schopnosti zachĂĄzet s korelacĂ mezi pĆĂznaky. VĆĄechny tĆi metody byly otestovĂĄny na veĆejnĂ© databĂĄzi 254 multisekvenÄnĂch MR obrazech a byla dosĂĄhnuta pĆesnost srovnatelnĂĄ s nejmodernÄjĆĄĂmi metodami v mnohem kratĆĄĂm vĂœpoÄetnĂm Äase (v ĆĂĄdu sekund pĆi pouĆŸitĂœ CPU), coĆŸ poskytuje moĆŸnost manuĂĄlnĂch Ășprav pĆi interaktivnĂ segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.
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