Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain

  title={Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain},
  author={Ivica Kopriva and Ante Jukic and Xinjian Chen},
  booktitle={Medical Imaging},
A method is proposed for unsupervised 3D (volume) segmentation of registered multichannel medical images. To this end, multichannel image is treated as 4D tensor represented by a multilinear mixture model, i.e. the image is modeled as weighted linear combination of 3D intensity distributions of organs (tissues) present in the image. Interpretation of this model suggests that 3D segmentation of organs (tissues) can be implemented through sparseness constrained factorization of the nonnegative… 

A hybrid approach for detection of brain tumor in MRI images

This method is used to detect brain tumor in MRI images by combining Clustering and Classification methods to decrease the complexity of time and memory and has achieved a fast speed for segmentation of MRI 3D images.

Nonnegative matrix factorization: When data is not nonnegative

  • Siyuan WuJim Wang
  • Computer Science
    2014 7th International Conference on Biomedical Engineering and Informatics
  • 2014
A new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values by developing a new method that only allows W to contain nonnegative values, but allows both X and H to have both nonnegative and negative values.

Image Processing

This poster presents a meta-anatomical reconstruction of the response of the immune system to laser-spot assisted treatment of central giant cell granuloma.



Nonlinear band expansion and nonnegative matrix underapproximation for unsupervised segmentation of a liver from a multi-phase CT image

A methodology is proposed for contrast enhanced unsupervised segmentation of a liver from a twodimensional multi-phase CT image that exploits concentration and spatial diversities between organs present in the image and consists of nonlinear dimensionality expansion followed by matrix factorization that relies on sparseness between spatial distributions of organs.

Nonlinear Band Expansion and 3D Nonnegative Tensor Factorization for Blind Decomposition of Magnetic Resonance Image of the Brain

Efficiency of the N BE-NTF algorithm is demonstrated over NBE-ICA and NTF-only algorithms on blind decomposition of the realistically simulated MRI of the brain.

Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization

This paper proposes to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined and overdetermined model.

Blind decomposition of low‐dimensional multi‐spectral image by sparse component analysis

A multilayer hierarchical alternating least square nonnegative matrix factorization approach has been applied to blind decomposition of low‐dimensional multi‐spectral image. The method performs blind

Using underapproximations for sparse nonnegative matrix factorization

Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMFs various extensions and modifications, especially Nonnegative Tensor

Independent Component Analysis

The standardization of the IC model is talked about, and on the basis of n independent copies of x, the aim is to find an estimate of an unmixing matrix Γ such that Γx has independent components.

Adaptive blind signal and image processing

Find the secret to improve the quality of life by reading this adaptive blind signal and image processing and make the words as your good value to your life.

Some mathematical notes on three-mode factor analysis

The model for three-mode factor analysis is discussed in terms of newer applications of mathematical processes including a type of matrix process termed the Kronecker product and the definition of