Simultaneous Sparsity Model for Histopathological Image Representation and Classification

Srinivas, Umamahesh; Mousavi, Hojjat Seyed; Monga, Vishal; Hattel, Arthur L.; Jayarao, Bhushan M.

doi:10.1109/tmi.2014.2306173

Cited by 90 publications

(64 citation statements)

References 49 publications

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“…Similar to the SHIRC model proposed in [16], [18], we can impose one constraint on active elements of tensor x as follows: x also has few nonzero tubes as in SRC-CC; however, elements in one active tube are not necessarily the same. In other words, the locations of nonzero coefficients of training samples in the linear combination exhibit a oneto-one correspondence across channels.…”

Section: Generalized Sparse Representation-based Classificationmentioning

confidence: 99%

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Nguyen

Monga

2019

IEEE Trans. Aerosp. Electron. Syst.

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Using low-frequency (UHF to L-band) ultrawideband (UWB) synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data but also take advantage of the polarization diversity and the aspect angle dependence information from multi-channel SAR data. First, the traditional sparse representation-based classification (SRC) is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Lastly, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the dataset we consider. Extensive experimental results on a high-fidelity electromagnetic simulated dataset and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.

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Section: Generalized Sparse Representation-based Classificationmentioning

confidence: 99%

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Nguyen

Monga

2019

IEEE Trans. Aerosp. Electron. Syst.

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“…There are many popular features and image descriptors which are being used today, including scale invariant feature transform (SIFT), histogram of oriented gradient (HOG), bag of words (BoW) [15]- [17], etc. Geometrical features and sparsitybased features are also used for some biometric and medical applications in several works [18]- [20]. A new algorithm for feature selection for small datasets is presented in [21].…”

Section: Featuresmentioning

confidence: 99%

Iris recognition using scattering transform and textural features

Minaee

Abdolrashidi

Wang

2015

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

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Iris recognition has drawn a lot of attention since the midtwentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

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“…Recently, sparsity constrained learning methods have been developed for image classification [19] and found to be widely successful in medical imaging problems [20]–[23]. The essence of the aforementioned sparse representation based classification (SRC) is to write a test image (or patch) as a linear combination of training images collected in a matrix (dictionary), such that the coefficient vector is determined under a sparsity constraint.…”

Section: Introductionmentioning

confidence: 99%

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans

2018

IEEE Trans. Biomed. Eng.

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Objective Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs. post-op) plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-operative (post-op) computational tomographic (CT)-scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. Methods Our contributions include: 1.) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes, 2.) quantification of associated computation and memory footprint, and 3.) a customized training and test procedure for segmenting post-op hydrocephalic CT images. Results Experiments performed on infant CT brain images acquired from the CURE Children’s Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against number of training samples, enhancing its deployment potential.

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Simultaneous Sparsity Model for Histopathological Image Representation and Classification

Cited by 90 publications

References 49 publications

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Iris recognition using scattering transform and textural features

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans

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