Local binary patterns variants as texture descriptors for medical image analysis

By Loris Nanni, Alessandra Lumini, Sheryl Brahnam. Artif. Intell. Medicine, 2010

529 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1a93f275e360f5b0d2d6c67644611e24850d3144

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Texture Feature Extraction Methods: A Survey (2019, methodology): “LBP-based features have also been used for medical image analysis [93].”
Local binary features for texture classification: Taxonomy and experimental study (2017, background): “…[42] Multiscale Block LBP (MBLBP) [43], Three Patch LBP (TPLBP) [44] Four Patch LBP (FPLBP) [44], Pixel to Patch (PTP) [39], and Patterns of Oriented Edge Magnitudes (POEM) [45], Binary Rotation In-variant and Noise Tolerant (BRINT) [30], and very recently Median Robust Extended LBP (MRELBP) [46 .”
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, unknown): “The creators of EQP evaluated different topology patterns for the neighborhood of the central pixel, instead of considering only the circular neighborhood, as originally done for LBP and LTP.”
Extended local binary patterns for texture classification (2012, unknown): “Due to its impressive computational efficiency and good texture discriminative property, the dense LBP descriptor [4] has gained considerable attention since its publication [13], and has already been used in many other applications, including visual inspection, image retrieval, dynamic texture reco”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, background): “Nanni et al. [30] investigated the use of different neighborhood topologies (circle, ellipse, parabola, hyperbola and Archimedean spiral) and encodings in their research on LBP variants for medical image texture analysis.”
Discrimination between tumour epithelium and stroma via perception-based features (2015, background): “Among them, Local Binary Patterns (LBP) and variants have received a great deal of attention due to their high discrimination capability, ease of implementation and low computational cost [9, 10, 11].”
DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017, methodology): “At present, extracted features fall under the following categories: handcrafted features describing morphology and chromatin characteristics [11], [17]–[20] in accordance with “The Bethesda System (TBS)” rules [27], engineered features representing texture distribution [22], [23], [25] according to ”