Wavelet optimization for content-based image retrieval in medical databases

Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian

doi:10.1016/j.media.2009.11.004

Cited by 175 publications

(85 citation statements)

References 60 publications

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“…In retrieval applications, the most similar results are expected within the first few retrieved images as evidenced by the use of precision at 4 or 5 used by Shyu et al [100] and Quellec et al [116]; the CAPP graph is more capable than the complete graph at meeting this criterion.…”

Section: Discussionmentioning

confidence: 70%

“…Unsupervised classification was used to index heterogeneous information (in the form of wavelets [116] and semantic text data) on decision trees in [117]. A committee of decision trees was used to ensure that individual attributes (either text or image features) were not weighted too highly.…”

Section: Retrieval Enhancement Using Non-image Datamentioning

confidence: 99%

“…As in [116,118], the types of images contained by a data set determined its feature set, e.g., texture features for CT images, SUV features for PET images, area for 2D images, and volume for 3D images, etc. This enabled the use of features that exploited the characteristics of a particular data set.…”

Section: Graph Attributes and Image Features 631 Types Of Featuresmentioning

confidence: 99%

“…We compared the retrieval results to the reference index for each data set. We evaluated our work using precision and recall, two standard metrics used to evaluate CBIR research (e.g., [25,82,116,121,183]). Precision is the proportion of retrieved images that are similar to the query, while recall is the proportion of similar images in the database that were retrieved.…”

Section: Experimental Procedures 721 Determining the Relevance Of Rementioning

confidence: 99%

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A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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Section: Discussionmentioning

confidence: 70%

Section: Retrieval Enhancement Using Non-image Datamentioning

confidence: 99%

Section: Graph Attributes and Image Features 631 Types Of Featuresmentioning

confidence: 99%

Section: Experimental Procedures 721 Determining the Relevance Of Rementioning

confidence: 99%

See 2 more Smart Citations

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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“…Global feature category includes color histogram [3,4], texture histogram [7], color layout [5] of the whole image, and features selected from multidimensional discriminant analysis of a collection of images [23]. While local feature category includes color, texture [9], and shape features for sub images, segmented regions [2,6], or interest points. Color histogram tells the global distribution of colors in the images.…”

Section: Related Workmentioning

confidence: 99%

Correlated Networks for Content Based Image Retrieval

Irtaza¹,

Jaffar²,

Aleisa³

2013

IJCIS

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Efficient CBIR systems are based on three things, (1) how they represent the repository images in the form of signature; (2) how they measure the similarity of the database images with query image, (3) how they retrieve the semantically similar images in response of a query image. The paper is focusing on these three things. For signature development, curvelet transform, wavelet packets, and Gabor filters based signature development is introduced. For measuring the similarity, Pearson correlation is used as a distance measure; and for retrieving the semantically similar images in response of query images, Neural Network based architecture for content based image retrieval is presented. These things ensure the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better than all comparative systems.

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Automated detection of circulating tumor cells with naive Bayesian classifiers

et al. 2014

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Personalized medicine is a modern healthcare approach where information on each person's unique clinical constitution is exploited to realize early disease intervention based on more informed medical decisions. The application of diagnostic tools in combination with measurement evaluation that can be performed in a reliable and automated fashion plays a key role in this context. As the progression of various cancer diseases and the effectiveness of their treatments are related to a varying number of tumor cells that circulate in blood, the determination of their extremely low numbers by liquid biopsy is a decisive prognostic marker. To detect and enumerate circulating tumor cells (CTCs) in a reliable and automated fashion, we apply methods from machine learning using a naive Bayesian classifier (NBC) based on a probabilistic generative mixture model. Cells are collected with a functionalized medical wire and are stained for fluorescence microscopy so that their color signature can be used for classification through the construction of Red-Green-Blue (RGB) color histograms. Exploiting the information on the fluorescence signature of CTCs by the NBC does not only allow going beyond previous approaches but also provides a method of unsupervised learning that is required for unlabeled training data. A quantitative comparison with a state-of-the-art support vector machine, which requires labeled data, demonstrates the competitiveness of the NBC method. (5-7), much emphasis has been put on techniques that effectively collect CTCs from peripheral blood. Currently, the majority of studies use anti-epithelial-cell-adhesionmolecule (EpCAM) antibody-coated isolation systems (8-10), whereas other types of immunomagnetic devices (11-13), density gradient centrifugation (14) or membrane filtration (15,16), are also used for CTC enrichment. The subsequent CTC detection is realized by either immunocytological staining or polymerase chain reaction (17). Due to the extraordinary rarity of CTCs in patients, which have a typical ratio of one CTC per 10 8 blood cells (18), classical flow cytometry has proven unreliable and methods using intravital multiphoton flow cytometry and photoacoustic flowmetry are currently being developed (17,(19)(20)(21)(22). Although these methods have already successfully identified CTCs in control blood specimens spiked with cancer cell lines, they are not yet ready for use in detection of CTC in patients with cancer.The data of this study were generated by a technology that can collect CTCs in vivo using a functionalized and structured medical wire (FSMW) (23). Medical guidewires are widely used for angiography and insertion of venous catheters (24)

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Wavelet optimization for content-based image retrieval in medical databases

Cited by 175 publications

References 60 publications

A graph-based approach for the retrieval of multi-modality medical images

A graph-based approach for the retrieval of multi-modality medical images

Correlated Networks for Content Based Image Retrieval

Automated detection of circulating tumor cells with naive Bayesian classifiers

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