Edge extraction method for medical images based on improved local binary pattern combined with edge-aware filtering

Qiao, Sanzheng; Yu, Qinghan; Zhao, Zhengwei; Song, Liying; Tao, Hui; Zhang, Tian; Zhao, Chenglin

doi:10.1016/j.bspc.2022.103490

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…Further the image is rescaled to 52x48. From this rescaled image 9 descriptors are evaluated and these are LBP [14], HELBP [15], VELBP [15], NI-LBP [16], RD-LBP [16], MRELBP-NI [12], LPQ1 [13], LPQ2 [13] and NABILD. The compared descriptors generates size of 256 and NABILD produces size of 768.…”

Section: Feature Size Informationmentioning

confidence: 99%

NABILD: Noise And Blur Invariant Local Descriptor for Face Recognition

Karanwal

Diwakar²

2022

J. Phys.: Conf. Ser.

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Most of the existing local descriptors unable to perform well in front of noise and blur variations. Additionally there are very less descriptors persist in literature which are noise and blur invariant. To remedy this challenge the proposed work launch novel descriptor under noise and blur changes so-called Noise and Blur Invariant Local Descriptor (NABILD). With respect to two artificial noises i.e. Gaussian White Noise (GWN) and Salt & Pepper Noise (SPN) with artificial image blurring, the NABILD is introduced. Precisely NABILD takes essentials of two well performed descriptors with respect to noise and blur variations. The first one is Median Robust Extended LBP based on Neighborhood Intensity (MRELBP-NI) and second one is Multiscale Local Phase Quantization (MLPQ). MRELBP-NI is very effective in controlling GWN and SPN due to the capturing of microstructure and macrostructure information. LPQ is the efficient blur invariant descriptor as it quantizes the phase. By considering the merits of both of these descriptors their features are integrated into one framework called as NABILD. To lower down the feature dimension FLDA is deployed and classification is conducted by SVMs. Experiments on ORL face dataset confirm strength of NABILD against other tested descriptors in noise and blur variations. Various literature methods are also outclassed by NABILD.

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Section: Feature Size Informationmentioning

confidence: 99%

NABILD: Noise And Blur Invariant Local Descriptor for Face Recognition

Karanwal

Diwakar²

2022

J. Phys.: Conf. Ser.

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“…Modern diagnosis and the potential applications of edge extraction technologies require edge-based processing and analysis of medical images. Edge-aware filtering and improved Local Binary Patterns (EF-ALBP) were proposed by Qiao et al (2022).…”

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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“…Its processing object is the grayscale distance map, forming the dividing line between two different regions with similar greyscale distances, while the adhesive region of the seed is not segmented because it is in the same region with similar greyscale gradients, resulting in undersegmentation phenomenon. The edge detection algorithm can strengthen the correct edge, but it has a poor ability to process complex adhesions [ 14 ]. Introducing this algorithm into the watershed algorithm can suppress its response to weak edges and preserve the correct edges.…”

Section: Introductionmentioning

confidence: 99%