Spoke-LBP and ring-LBP: New texture features for tissue classification

Wan, Sunhua; Huang, Xiaolei; Lee, Hsiang‐Chieh; Fujimoto, James G.; Zhou, Chao

doi:10.1109/isbi.2015.7163848

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…The SIFT and SURF features were later overperformed by the oriented fast and rotated brief (ORB) method due to its comparable performance, robustness to noise, and less requirement of computational power [14]. The morphological operations were then followed by a wavelet-based covariance descriptor [15], wavelet neural network [16], spoke LBP, and ring LBP [17]. In [18], one class kernel principle component analysis (KPCA) model was proposed in which different features were extracted from each image in the class and one KPCA model was trained for each extracted feature separately.…”

Section: Relevant Studiesmentioning

confidence: 99%

“…A research group from Australia utilizes the combination of CNN with different techniques of local features extraction (LBP, contourlet transform (CT), histogram (H), discrete Fourier transform (DFT), and discrete cosine transform (DCT)) and demonstrated that the CNN with CT and H jointly that provides the best results over the BreakHis dataset with 200× magnification factor [40]. Despite the great clinical significance of multi-classification in providing a reliable diagnosis, most of the research works have been carried out only for binary classification [12,17,20,31,33,40]. From the abundant studies on BC histopathological images classification, a very small portion is devoted to multi-classification [34,36,38,39].…”

Section: Relevant Studiesmentioning

confidence: 99%

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Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

2020

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Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis and prognosis of breast cancer. In this work, two machine learning approaches are thoroughly explored and compared for the task of automatic magnification-dependent multi-classification on a balanced BreakHis dataset for the detection of breast cancer. The first approach is based on handcrafted features which are extracted using Hu moment, color histogram, and Haralick textures. The extracted features are then utilized to train the conventional classifiers, while the second approach is based on transfer learning where the pre-existing networks (VGG16, VGG19, and ResNet50) are utilized as feature extractor and as a baseline model. The results reveal that the use of pre-trained networks as feature extractor exhibited superior performance in contrast to baseline approach and handcrafted approach for all the magnifications. Moreover, it has been observed that the augmentation plays a pivotal role in further enhancing the classification accuracy. In this context, the VGG16 network with linear SVM provides the highest accuracy that is computed in two forms, (a) patch-based accuracies (93.97% for 40×, 92.92% for 100×, 91.23% for 200×, and 91.79% for 400×); (b) patient-based accuracies (93.25% for 40×, 91.87% for 100×, 91.5% for 200×, and 92.31% for 400×) for the classification of magnification-dependent histopathological images. Additionally, "Fibro-adenoma" (benign) and "Mucous Carcinoma" (malignant) classes have been found to be the most complex classes for the entire magnification factors.

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Section: Relevant Studiesmentioning

confidence: 99%

Section: Relevant Studiesmentioning

confidence: 99%

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

2020

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“…In breast cancer histopatholoy image analysis, convolutional neural networks are used for region of interest detection [19], segmentation [20], and also for mitosis detection [15]. On the other hand, for classification purposes, hand-crafted features are often employed [5], [21], [2], [7]. They include complex preprocessing pipeline including stain normalization, nucleus detection, and region of interest segmentation.…”

Section: Methodsmentioning

confidence: 99%

Deep learning for magnification independent breast cancer histopathology image classification

Bayramoglu

Kannala

Heikkilä

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

318

184

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Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.

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“…However, as the dimension of the LBP features increases with number of neighborhoods, this method requires more time for feature calculation than the traditional LBP. Wan et al proposed a block-based LBP (BLBP) for tissue classification [33], which compares the average intensities of the pixels in a fixed area around the center pixel. Accordingly, the BLBP feature can represent the global texture information.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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Spoke-LBP and ring-LBP: New texture features for tissue classification

Cited by 16 publications

References 20 publications

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

Deep learning for magnification independent breast cancer histopathology image classification

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

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