Erratum to “A review of content-based image retrieval systems in medical applications—Clinical benefits and future directions” [Int. J. Med. Inform. 73 (1) (2004) 1–23]

Müller, Henning; Michoux, Nicolas; Bandon, David; Geissbühler, Antoine

doi:10.1016/j.ijmedinf.2009.05.001

Cited by 32 publications

(8 citation statements)

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“…Imaging, computing and analyzing living organs have become a highly active research field [14,15]. Figure 15 …”

Section: Biological Tissues and Organsmentioning

confidence: 99%

“…Examples of NSD systems are encountered in astrophys [9], oceanography [10], rock at large scales [11,12], spatial patterns of environmental pollution [13], and biological tissues and organs [14]. In addition, medical diagnostics based on computations with threedimensional (3D) images [15] that are often nonstationary have become increasingly important.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Tahmasebi

Sahimi

2015

Phys. Rev. E

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Nonstationary disordered materials and media -those for which the probability distribution function of any property varies spatially when shifted in space -are abundant and encountered in astrophysics, oceanography, air pollution patterns, large-scale porous media, biological tissues and organs, and composite materials. Their reconstruction and modeling is a notoriously difficult and largely unsolved problem. We propose a method for reconstructing a broad class

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“…Imaging, computing and analyzing living organs have become a highly active research field [14,15]. Figure 15 …”

Section: Biological Tissues and Organsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Tahmasebi

Sahimi

2015

Phys. Rev. E

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“…Materials with similar microstructural features generally have similar properties. In medical fields, diagnostic decisions for a non‐prior‐knowledge case are often made through retrieving similar cases from the medical image database . This application of image retrieval offers the inspiration upon which similarity analysis regarding materials’ microstructure images in this work is proposed.…”

Section: Introductionmentioning

confidence: 99%

Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterization

Wang

Ogawa

Adachi

2020

Advcd Theory and Sims

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Materials with similar microstructural features exhibit similar properties, a fact which often provides useful insights for a detailed understanding of the materials. An analysis of material similarity in terms of microstructural images is proposed for predicting some properties of interest. This similarity analysis is inspired by the application of medical image retrieval to guide diagnostic decisions. Some relevant analyzing techniques including machine‐learning algorithms of zero‐normalized cross‐correlation, mutual information, maximum likelihood estimation, principal component analysis, and self‐organizing map are applied in this work. These techniques are systematically employed to identify the variances of the query images based on the metrics of image natural properties (such as brightness which is measured in pixel) or metallurgical features contained in the microstructural images. It is shown that the employed methods exhibit consistent similarity evaluation results. The proposed similarity analysis of microstructural images is expected to provide a new avenue for understanding the materials paradigm.

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“…The current digital technologies for computing, networking, and database storage have empowered automated search for relevant and visually similar references in large image databases. The importance of image similarity for diagnostic decision making has activated wide research interest in the development of ContentBased Image Retrieval (CBIR) technology in medical imaging areas [9], [10]. Several groups are developing methods to incorporate CBIR approaches into image database systems [11]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Similarity Study of Interactive Content-Based Image Retrieval Scheme for Classification of Breast Lesions

Cho

Hadjiiski

Sahiner

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYTo study the similarity between queries and retrieved masses, we design an interactive CBIR (Content-based Image Retrieval) CADx (Computer-aided Diagnosis) system using relevance feedback for the characterization of breast masses in ultrasound (US) images based on radiologists' visual similarity assessment. The CADx system retrieves masses that are similar to query masses from a reference library based on six computer-extracted features that describe the texture, width-to-height, and posterior shadowing of the mass. The k-NN retrieval with Euclidean distance similarity measure and the Rocchio relevance feedback algorithm (RRF) are used. To train the RRF parameters, the similarities of 1891 image pairs from 62 (31 malignant and 31 benign) masses are rated by 3 MQSA (Mammography Quality Standards Act) radiologists using a 9-point scale (9=most similar). The best RRF parameters are chosen based on 3 observer experiments. For testing, 100 independent query masses (49 malignant and 51 benign) and 121 reference masses on 230 (79 malignant and 151 benign) images were collected. Three radiologists rated the similarity between the query masses and the computer-retrieved masses. Average similarity ratings without and with RRF were 5.39 and 5.64 for the training set and 5.78 and 6.02 for the test set, respectively. Average AUC values without and with RRF were, respectively, 0.86 ± 0.03 and 0.87 ± 0.03 for the training set and 0.91±0.03 and 0.90±0.03 for the test set. On average, masses retrieved using the CBIR system were moderately similar to the query masses based on radiologists' similarity assessments. RRF improved the similarity of the retrieved masses.

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Erratum to “A review of content-based image retrieval systems in medical applications—Clinical benefits and future directions” [Int. J. Med. Inform. 73 (1) (2004) 1–23]

Cited by 32 publications

References 0 publications

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterization

A Similarity Study of Interactive Content-Based Image Retrieval Scheme for Classification of Breast Lesions

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