An Efficient Retrieval System for Biomedical Images Based on Radial Associated Laguerre Moments

Hassan, Gaber; Hosny, Khalid M.; Farouk, R. M.; Alzohairy, Ahmed M.

doi:10.1109/access.2020.3026452

Cited by 22 publications

(13 citation statements)

References 60 publications

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“…Results of retrieval are used for retuning the model, and estimating the best hyperparameters for efficient model performance. Due to which, the proposed model is 18% more accurate than GRH [2], 36% more accurate than RaLM [5], and 34% more accurate than DHV [16], while it has 14% more recall than GRH [2], 19% more recall than RaLM [5], and 29% more recall than DHV [16] for different types of medical images. Similar observations were obtained for precision, fMeasure and AUC performance, which indicates that the model is useful for a wide variety of medical image retrieval applications.…”

Section: Conclusion and Future Scopementioning

confidence: 97%

“…Each Parametric analysis in terms of accuracy (A), precision (P), recall (R), area under the curve (AUC), and fMeasure (F) values. Each of these parameters are estimated for GRH [2], RaLM [5], DHV [16] From the precision evaluation, it is observed that the proposed model is 14% more precise than GRH [2], 16% more precise than RaLM [5], and 24% more precise than DHV [16] for different types of medical images. This improvement in precision is due to use of application specific retrieval method selection, which makes the model highly useful for clinical applications.…”

Section: Design Of Performance Validation and Model Retuning Unit Usi...mentioning

confidence: 99%

“…These models have good performance for a specific pathological image type, and thus cannot be extended to other applications without compromising on retrieval quality. Similarly, the work in [5,6] propose use of Radial associated Laguerre Moments (RaLM), and relevance feedback-based mechanism for multiple types of medical images. These models showcase moderate performance on different image types, and thus are useful for coarse grained image retrieval.…”

Section: Introductionmentioning

confidence: 99%

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Design of a High Efficiency Cross-Domain Medical Image Retrieval System Using Augmented Transfer Learning

Ahmed¹,

Zuber²,

Aamer³

2022

Preprint

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Retrieving Medical Images from a large inter-domain dataset requires multiple high efficiency processing models. These models include, but are not limited to, image classification, domain specific feature extraction & selection, ranking and post processing. A wide variety of system models have been designed to perform these tasks, but have limited accuracy, and retrieval performance due to improper cross-domain feature processing. In order to improve performance of cross-domain medical image retrieval systems, this text proposes a transfer learning mechanism, that learns from features of one domain, and applies the trained models to other domains. The proposed method uses a combination of VGGNet19, AlexNet, InceptionNet and Xception Net models for ensemble learning, along with wavelet and bag of features (WBoF) for efficient feature extraction. Each of the individual models were applied to different medical domains, and their retrieval accuracies were evaluated. Based on this evaluation, it is observed that VGGNet19 has better performance on Computer Tomography (CT) images, AlexNet model has better performance on Magnetic Resonance Imaging (MRI) images, InceptionNet model has better performance on Positron Emission Tomography (PET) images, while Xception Net has better retrieval performance for ultrasound (USG) images. Using this observation, a highly efficient augmentation model is designed, which achieves an accuracy of 98.06%, precision of 65.9%, recall of 76.1%, and area under the curve (AUC) performance of 98.9% on different datasets. This performance is compared with a wide variety of medical image datasets including Center for Invivo Microscopy (CIVM), Embrionic and Neonatal Mouse (H&E, MR), LONI image data archive, The Open Access Series of Imaging Studies (OASIS), & CT scans for Colon Cancer (CSCC). It was observed that the proposed model outperforms most of the recent state-of-the-art models, and achieves consistent parametric results across multiple domain medical images.Categories: H.3.1, H.3.2, H.3.3, H.3.7, H.5.1

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Section: Conclusion and Future Scopementioning

confidence: 97%

Section: Design Of Performance Validation and Model Retuning Unit Usi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a High Efficiency Cross-Domain Medical Image Retrieval System Using Augmented Transfer Learning

Ahmed¹,

Zuber²,

Aamer³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The study of the literature on image analysis techniques indicates that the method of orthogonal moments plays a significant role in each of its important fields. These fields include image reconstruction [1,2], face recognition [3], image classification [4,5], image watermarking [6], image encryption [7], image compression [8,9]. Orthogonal moments are classified as continuous or discrete depending on whether the kernel functions are orthogonal in the continuous or discrete domain.…”

Section: Introductionmentioning

confidence: 99%

“…Square Error (MSE): the reconstruction error between the original and reconstructed signals. 𝑠(𝑥) − 𝑆(𝑥))2 …”

mentioning

confidence: 99%

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

Fathi

Ahmed

Makhlouf

2022

Preprint

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Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete Orthogonal Moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram-Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error (MSE ) and peak signal to noise ratio ( PSNR). The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram-Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.

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CUDAQuat: new parallel framework for fast computation of quaternion moments for color images applications

et al. 2021

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An Efficient Retrieval System for Biomedical Images Based on Radial Associated Laguerre Moments

Cited by 22 publications

References 60 publications

Design of a High Efficiency Cross-Domain Medical Image Retrieval System Using Augmented Transfer Learning

Design of a High Efficiency Cross-Domain Medical Image Retrieval System Using Augmented Transfer Learning

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

CUDAQuat: new parallel framework for fast computation of quaternion moments for color images applications

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