Content-based image retrieval of multiphase CT images for focal liver lesion characterization.

@article{Chi2013ContentbasedIR,
  title={Content-based image retrieval of multiphase CT images for focal liver lesion characterization.},
  author={Yanling Chi and Jiayin Zhou and Sudhakar Kundapur Venkatesh and Qi Tian and Jimin Liu},
  journal={Medical physics},
  year={2013},
  volume={40 10},
  pages={
          103502
        }
}
PURPOSE Characterization of focal liver lesions with various imaging modalities can be very challenging in the clinical practice and is experience-dependent. The authors' aim is to develop an automatic method to facilitate the characterization of focal liver lesions (FLLs) using multiphase computed tomography (CT) images by radiologists. METHODS A multiphase-image retrieval system is proposed to retrieve a preconstructed database of FLLs with confirmed diagnoses, which can assist radiologists… 
Bag of temporal co-occurrence words for retrieval of focal liver lesions using 3D multiphase contrast-enhanced CT images
TLDR
The preliminary results show that the proposed BoTCoW method outperforms the previously proposed temporal features and multiphase features based on the BoVW model.
A retrieval system for 3D multi-phase contrast-enhanced CT images of focal liver lesions based on combined bags of visual words and texture words
  • Yingying Xu, Lanfen Lin, Yen-Wei Chen
  • Computer Science
    2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
  • 2016
TLDR
This paper combines texture, density and shape features for CBMIR based on 3D multi-phase contrast enhanced CT images according to radiologists' clinical experience and implements bag of visual words (BoVW) model to extract texture features from FLLs based on 2D local binary pattern (LBP) and combined with conventional intensity-based BoVW.
Three-Dimensional Spatio-Temporal Features for Fast Content-based Retrieval of Focal Liver Lesions
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A 3D image-based spatio-temporal feature extraction framework for fast content-based retrieval of focal liver lesions and experiments show that the proposed system’s query processing is more than 20 times faster than other already published systems that use 2D features.
Three-Dimensional Spatiotemporal Features for Fast Content-Based Retrieval of Focal Liver Lesions
TLDR
A3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions with fast computation time and high retrieval accuracy has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.
Combined Density, Texture and Shape Features of Multi-phase Contrast-Enhanced CT Images for CBIR of Focal Liver Lesions: A Preliminary Study
TLDR
A novel 3D shape feature is proposed for CBIR of focal liver lesions based on combined density, texture and shape features of multi-phase contrast-enhanced CT volumes in order to improve the retrieval accuracy and reduce the computation time.
M-DFNet: Multi-phase Discriminative Feature Network for Retrieval of Focal Liver Lesions
TLDR
The M-DFNet is designed to cope with multi-phase information and the FRModule is proposed to recalibrate the deep features based on the learned class centers to tackle the complex imaging manifestations of FLLs and further enhance both the feature discrimination and generalization.
Multiphase Focal Liver Lesions Classification with Combined N-gram and BoVW
TLDR
This work proposes a novel model for multiphase medical image feature generation named the Bi-gram bag-of-spatiotemporal words (Bi-gram BoSTW) to capture the temporal information, as well as, the spatial co-occurrence relationship of the lesion to introduce visual N-grams scheme to contrast-enhanced CT images.
Texture analysis as a tool for medical decision support. P. 2 Classification of liver disorders based on computed tomography images
TLDR
An overview of the texture analysis methods, that have been applied for hepatic tissue characterization from Computed Tomography (CT) images, including details of about forty studies, presented over the past two decades, devoted to (semi)automatic detection or/and classification of different liver pathologies.
Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images
TLDR
A novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis and preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions.
Automated characterisation and classification of liver lesions from CT scans
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The research gap is bridged if a relation of image contents to medical meaning in analogy to radiologist understanding is established and the efficacy of the proposed framework in the successful characterisation and classification of the liver lesions in CT scans is demonstrated.
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References

SHOWING 1-10 OF 32 REFERENCES
Liver tumor detection and classification using content-based image retrieval
TLDR
The proposed method first detects liver abnormalities by eliminating the normal tissue/organ from the liver region, and in the second step it ranks these abnormalities with respect to spherical symmetry, compactness and size using a tumoroid measure to facilitate fast location of liver focal mass lesions.
Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.
TLDR
Preliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.
Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images
TLDR
Preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
Computer-aided focal liver lesion detection
TLDR
An automatic method which can detect diverse focal liver lesions in 3D CT volumes and use a discriminative approach to suppress false positives with the advantage of tumoroid, a novel measurement combining three shape features spherical symmetry, compactness and size is developed.
Content-Based Image Retrieval in Radiology: Current Status and Future Directions
TLDR
By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.
A Discriminative Distance Learning-Based CBIR Framework for Characterization of Indeterminate Liver Lesions
TLDR
A novel learning---based CBIR method for fast content---based retrieval of similar 3D images based on the intrinsic Random Forest similarity is proposed and the impact of high---level concepts on the quality and relevance of the retrieval results has been measured and is discussed for three different set---ups.
Liver CT image retrieval based on non-tensor product wavelet
TLDR
Experimental results show that this content-based medical image retrieval method can improve the detection rate of lesions and obtains good results in hepatic hemangioma and HCC which are difficult differential diagnosis both of rich blood supply to tumors.
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier
TLDR
A computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented and shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.
Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography.
TLDR
A computer-aided diagnostic scheme for classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, and three histologic differentiation types of hepatocellular carcinoma (HCC) by use of microflow imaging of contrast-enhanced ultrasonography has the potential to improve the diagnostic accuracy in the histologic diagnosis of HCCs and the other liver diseases.
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