Classification of Diffuse Lung Disease Patterns on High-Resolution Computed Tomography by a Bag of Words Approach

Xu, Rui; Hirano, Yasushi; Tachibana, Rie; Shoji, K.

doi:10.1007/978-3-642-23626-6_23

Cited by 27 publications

(18 citation statements)

References 12 publications

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“…Figure 2 (b) illustrates the regular sampling grids used in this work. In the preliminary work [17], we adopted the VOI's size and the grid step to be 32×32×16 and 4×4×4 respectively, and obtained 9 × 9 × 5 = 405 sampling points. We consider that it would be more reasonable to use an isosized VOI, so we set the VOI size to be 32×32×32 in this work.…”

Section: Bag-of-features Approachmentioning

confidence: 99%

“…In this paper, the experimental setting is totally different from our preliminary work [17]. The differences rely on both of the complete separation of training and testing data sets and the ways of tuning parameters.…”

Section: Experimental Setting and Tuning Parametersmentioning

confidence: 99%

“…We randomly divide training samples into 20 groups, each of which has similar relative ratios on samples of each class. In the preliminary work [17], we optimized parameters by a fix-one-then-turning-one method, which meant that we only adjust one parameter in each time while setting the others to be fixed values. For example, we initially adjust the first parameter by setting the others to be any certain values; when we fix the optimal value of the first parameter we use it and adjust the second one.…”

Section: Experimental Setting and Tuning Parametersmentioning

confidence: 99%

“…A preliminary version of this work was published [17]. The major differences against the preliminary version rely on two aspects.…”

Section: Introductionmentioning

confidence: 99%

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A Bag-of-Features Approach to Classify Six Types of Pulmonary Textures on High-Resolution Computed Tomography

Hirano

Tachibana

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYComputer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read highresolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigenvalues of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 32×32×32. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.

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Section: Bag-of-features Approachmentioning

confidence: 99%

Section: Experimental Setting and Tuning Parametersmentioning

confidence: 99%

Section: Experimental Setting and Tuning Parametersmentioning

confidence: 99%

“…A preliminary version of this work was published [17]. The major differences against the preliminary version rely on two aspects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bag-of-Features Approach to Classify Six Types of Pulmonary Textures on High-Resolution Computed Tomography

Hirano

Tachibana

et al. 2013

IEICE Trans. Inf. & Syst.

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“…In the following stages, the raw images are calculated and are used to improve the system performance. The following equations are known as a Hessian matrix [47].…”

Section: Feature Extractionmentioning

confidence: 99%

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Islam¹,

Raj

Mujtaba³

2017

Symmetry

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Abstract:The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the wellmaintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

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