X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns

Ko, Byoung Chul; Kim, Seong Hoon; Nam, Jae-Yeal

doi:10.1007/s10278-011-9380-3

Cited by 68 publications

(41 citation statements)

References 20 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sign component is equivalent to the conventional LBP where its calculation is given in (1). The magnitude component computes the difference between the centre pixel and neighbour pixel against the average difference of the image to capture only strong intensity differences in a local neighbourhood.…”

Section: mentioning

confidence: 99%

“…In order to enable efficient manipulation (e.g. : searching, browsing and retrieving), these images have to be pre-classified [1]. One of the most critical classification tasks in medical images is the classification of the chest X-ray (CXR), as it is the most common medical image modality acquired and accounts for around one third of all radiographs taken in a typical radiology department [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced local binary pattern for chest X-ray classification

Wong

Abu-Shareha

Pasha

et al. 2013

2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)

View full text Add to dashboard Cite

The Local Binary Pattern (LBP) descriptor encodes the complementary information of the spatial patterns and intensity variations in a local image neighborhood. The richness of this multidimensional information offers many possible variations to the encoding process. Taking advantage of this, several variants of the LBP have been proposed. This work attempts to further optimize the discriminative power of the LBP specifically for the medical image classification task. It proposes an LBP variant that takes into account the salient edge features that are found particularly in chest X-ray images. In addition, it introduces a semi-global histogram to replace the commonly used global histogram which normally represents the spatial distribution of the generated codes in the LBP encoding process. The proposed LBP variant has been applied to the task of classifying X-rays of the ImageCLEFmed 2009 dataset into different chest categories. Experimental results show that while reducing the computational load the proposed LBP variant achieved an accuracy of 99.19% as compared to the best reported LBP based results of 98.73%, in classifying chest x-ray images of the ImageCLEFmed 2009 dataset.

show abstract

Section: mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced local binary pattern for chest X-ray classification

Wong

Abu-Shareha

Pasha

et al. 2013

2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)

View full text Add to dashboard Cite

show abstract

“…LBP pattern extraction from the wavelet domain can also reduce noise because LBP and CS-LBP are suitable for modeling repetitive textures, which means they are sensitive to random noise in uniform image areas. 18 Thus several researchers 18,19 have tried to extract LBP and CS-LBP features from the wavelet-transformed domain. Therefore we extract a CS-LBP descriptor from two wavelet-transformed subimages, rather than LBP descriptors, which reduces the feature dimension.…”

Section: Cs-lbp Feature Extraction Using a Saliencymentioning

confidence: 99%

“…In addition, the RF classifier has the capacity to process huge amounts of data with high training speeds and better performance than SVM, 18 because it is based on decision trees, so our proposed cascade of RFs also had better performance than SVM-based detection methods.…”

Section: Performance Comparison With Related Studiesmentioning

confidence: 99%

Three-level cascade of random forests for rapid human detection

Kim

Jung

et al. 2013

Opt. Eng

Self Cite

View full text Add to dashboard Cite

Abstract. We propose a novel human detection approach that combines three types of center symmetric local binary patterns (CS-LBP) descriptors with a cascade of random forests (RFs). To detect human regions in a lowdimensional feature space, we first extract three types of CS-LBP features from the scanning window of a downsampled saliency texture map and two wavelet-transformed subimages. The extracted CS-LBP descriptors are applied to a three-level cascade of RFs, which combines a series of RF classifiers as a filter chain. The three-level cascade of RFs with CS-LBPs delivers rapid human detection with higher detection accuracy, as compared with combinations of other features and classifiers. The proposed algorithm is successfully applied to various human and nonhuman images from the INRIA dataset, and it performs better than other related algorithms.

show abstract

“…This reduces the overall cost of computation without impacting much on the final accuracy [12], [3], [13], provided that the classifier delivers fast predictions and the pre-classification accuracy is good enough. A drawback of this approach is that errors occurring at the pre-classification stage might severely jeopardize the whole retrieval process.…”

Section: Introductionmentioning

confidence: 99%

An efficient radiographic Image Retrieval system using Convolutional Neural Network

Chowdhury¹,

Bulò²,

Moreno³

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

Abstract-Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain highlevel image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the "Image Retrieval in Medical Applications" (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

show abstract

X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns

Cited by 68 publications

References 20 publications

Enhanced local binary pattern for chest X-ray classification

Enhanced local binary pattern for chest X-ray classification

Three-level cascade of random forests for rapid human detection

An efficient radiographic Image Retrieval system using Convolutional Neural Network

Contact Info

Product

Resources

About