Mean distance local binary pattern: a novel technique for color and texture image retrieval for liver ultrasound images

Bedi, Anterpreet Kaur; Sunkaria, Ramesh Kumar

doi:10.1007/s11042-021-10758-7

Cited by 19 publications

(4 citation statements)

References 37 publications

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“…LNDP is combined with LBP to form a more robust feature descriptor. In [24], the Mean distance local binary pattern (Mean distance LBP) is proposed. Mean distance LBP is based on the Euclidean distance of the neighbouring pixels from the central pixels.…”

Section: Related Workmentioning

confidence: 99%

MVM-LBP : mean−variance−median based LBP for face recognition

Arora

Sucharitha

Sharma

2023

Int. j. inf. tecnol.

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This paper proposes a novel descriptor called Mean-Variance-Median based LBP (MVM-LBP). The Median binary pattern (MBP) calculates the difference between median of all the pixels in a 3 × 3 pixel window and respective eight neighbours is used. The main draw back of MBP is that two different pixel windows can have same MBP code which is not appropriate in face recognition systems. To improve the performance of the system, the proposed descriptor (MVM-LBP) involves more statistics and uses the mean, variance, and median values of all the pixels in a 3 × 3 pixel window to get a more robust feature vector. To check the excellence the of the proposed descriptor, the proposed method is applied on two publicly available facial data sets AT&T, and faces94. The outcomes show that the proposed descriptor gives encouraging results.

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Section: Related Workmentioning

confidence: 99%

MVM-LBP : mean−variance−median based LBP for face recognition

Arora

Sucharitha

Sharma

2023

Int. j. inf. tecnol.

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“…Other algorithms have been proposed to improve LBPbased results, such as the orthogonal combination of LBP extended to color spaces [55], the local combination adaptive ternary pattern that encodes both color and local information [56], the improved local ternary patterns extended for color properties [57], the color local pattern (CLP) [58], the multichannel adder-based LBP and multichannel decoderbased LBP (mdLBP) [59], the softly quantized color LBP [60], the local binary pattern for color images where a color pixel is treated as a vector having m-components and form a hyperplane [61], and more recently the left to right LBP, the top to down LBP, the curve surface LBP, and the cube diagonal LBP [62], the mean distance LBP combined with color features and co-occurrence matrix [63], the multiple channels LBP that uses both the correlation information among multiple color channels and characteristics in a single color channel [64], and new descriptors called LBPL and LBPL + LBPC that represent color cue as the correlation of pixels after deriving three regression lines in a local window and deriving LBP-like patterns [65].…”

Section: ) Conceptmentioning

confidence: 99%

Color Texture Analysis: A Survey

Humeau-Heurtier

2022

IEEE Access

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In the field of image processing, texture features and color are fundamental visual cue with complementary roles. They are used in many applications and in a large variety of areas such as quality control, contentbased image retrieval, remote sensing, industrial inspection, surface inspection, object recognition, and medical image analysis. For this purpose, a large number of algorithms have been proposed for texture feature extraction. Some of them are dedicated to gray-scale images while others aim at processing both color and texture. It has been shown that, for many cases, the use of color improves the performance of gray level texture classification. This paper provides a comprehensive survey of the texture feature extraction methods that consider both texture and color information. We propose a categorization of these methods into seven classes, two of them being very recent. For each method, we present the concept, the advantages and drawbacks, and we give examples of application.

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“…Mean distance LBP was proposed by Bedi and Sunkaria (2021). for texture retrieval from liver ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

P.,

2024

Journal of Computer Science

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The novel logarithmic transformation of Local Binary Pattern (LT-LBP) for texture feature extraction is used with Deep Learning (DL) models. The Computational complexity in the DL models asymptotically increases with the data sesssst and the depth of the layers in Convolutional Neural Networks (CNN). Pre-processing the images before training the DL model holds a strong impact on the operation speed and throughput. The proposed work encompasses LT-LBP for spatial texture feature extraction and is combined with the DL models. LT-LBP reduces the computation while extracting the texture information from the medical images. The computational cost for the model has been reduced in this study. The experiments were done with various DL models such as inception V3, Dense Net, Mobile Net, Efficient Net (B0-B7) VGG16, and RESNET50, and the results were analyzed. The features were extracted using LT-LBP and given as input to the DL models for classification. The retrospective study was done with 118 COVID-19 X-ray images collected from Chengalpattu Medical College Hospital, Chennai, and the remaining 8,224 images were taken from Kaggle. The combination of LT-LBP with RESNET50 shows better performance when compared to other models with an accuracy of 87%. The Area Under the Curve (AUC) for the proposed model is 88, 83 and 86% for COVID, non-COVID, and pneumonia classifications.

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Mean distance local binary pattern: a novel technique for color and texture image retrieval for liver ultrasound images

Cited by 19 publications

References 37 publications

MVM-LBP : mean−variance−median based LBP for face recognition

MVM-LBP : mean−variance−median based LBP for face recognition

Color Texture Analysis: A Survey

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

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