Medical Image Analysis Using AM-FM Models and Methods

Constantinou, Kyriacos P.; Constantinou, Ioannis; Pattichis, Constantinos S.; Pattichis, Marios S.

doi:10.1109/rbme.2020.2967273

Cited by 12 publications

(6 citation statements)

References 102 publications

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“…This section studies the performance efficiency of the proposed SFANR technique and existing noise removal techniques [1], [17]. This work uses brain MS lesion MRI data used in [30] which is very similar to brain MRI used [1], [17]. In this work noise such as Speckle, Gaussian, and Rician is added to the brain MS lesion MRI.…”

Section: Simulation Analysis and Resultsmentioning

confidence: 99%

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Swetha¹,

Aditya²

2022

IJACSA

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Medical Resonance Imaging (MRI) is nonradioactive-based medical imaging that provides a superresolution of tissues. However, because of its complex nature using existing Deep Learning-based noise removal (i.e., Denoising) techniques, the reconstruction quality is poor and time-consuming. An extensive study shows very limited work has been done on Brain Multiple Sclerosis (MS) Lesions MRI. Designing an efficient noise removal technique will aid in improving MRI quality; thereby will aid in achieving better segmentation classification performance. In reducing computing time and enhancing image quality (i.e. reduce noise) this paper presents the Sparse Feature Aware Noise Removal (SFANR) technique for Brain MRI using Convolution Neural Network (CNN) architecture. A sparse-aware feature is incorporated into the patch-wise morphology learning model for removing noise in large-scale MRI MS lesion datasets. Experimental results demonstrated that our model SFANR outperforms all other state-of-art noise removal techniques in terms of Peak-Signal-Noise-Ratio (PSNR), Structural Similarity Index Metric (SSIM) with less running time.

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Section: Simulation Analysis and Resultsmentioning

confidence: 99%

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Swetha¹,

Aditya²

2022

IJACSA

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“…The proposed model is implemented using Python 3 and MATLAB framework using Windows 10 operating system running on I-7 quad-core processor, with 16GB RAM and CUDA-enabled 4GB GPU. The MS lesion brain MRI is collected from [23,24] ISBI 2015 challenge dataset which provides similar data in [25,26]. The dataset has 19 patients' MRI scans of T1-weighted, T2-weighted, and FLAIR., out of which 5 MRIs annotated by two physicians are used for training, and the remaining MRIs are used for validating models.…”

Section: Resultsmentioning

confidence: 99%

Noise Invariant Convolution Neural Network for Segmentation of Multiple Sclerosis Lesions from Brain Magnetic Resonance Imaging

Swetha¹,

Aditya²

2022

Int. J. Onl. Eng.

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The objective of the research work is to accurately segment multiple sclerosis (MS) lesions in brain Magnetic Resonance Imaging (MRI) of varying sizes and also to classify its types. Designing effective automatic segmentation and classification tool aid the doctors in better understanding MS lesion progressions. In meeting research challenges, this paper presents Noise Invariant Convolution Neural Network (NICNN). The NICNN model is efficient in the detection and segmentation of MS lesions of varying sizes in comparison with standard CNN-based segmentation methods. Further, this paper introduced a new cross-validation scheme to address the class imbalance issue by selecting effective features for classifying the type of MS lesion. The experiment outcome shows the proposed method provides improved Dice Similarity Coefficient (DSC), Positive Predicted Value (PPV), and True Positive Rate (TPR) value compared to the state-of-art CNN-based MS lesion segmentation method. Further, achieves better accuracy in classifying MS lesion types compared to standard MS lesion type classification models.

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“…Similarly, amplitude components are also standardized due to scan normalization. Then, over each segmented region, we compute a multiscale AM-FM decomposition using [27] (also see [36]):…”

Section: F Am-fm Analysismentioning

confidence: 99%

“…where 𝑛 = 1, 2, 3 correspond to the low, medium, and high scales, 𝑎 𝑛 (𝑥, 𝑦) denote the instantaneous amplitude (IA) components, and 𝜑 𝑛 (𝑥, 𝑦) denote the instantaneous phase components. For each AM-FM component, we have the associated instantaneous frequency (IF): ∇𝜑 𝑛 (𝑥, 𝑦) that is estimated as described in [27].…”

Section: F Am-fm Analysismentioning

confidence: 99%

Multiple Sclerosis Disease Evolution Assessment in Brain MRI Lesions Based on Texture and Multi-Scale Amplitude Modulation-Frequency Modulation (AM-FM) Features

et al. 2023

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Background: Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). Methods: MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T2W) images obtained from serial brain MRI scans at the baseline (Time0M) and the repeat (Time6-12M) examinations. The subjects were separated into two different groups based on their EDSS: (G1: 1≤EDSS2Y≤3.5 (N=26) and G2: 3.5

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Medical Image Analysis Using AM-FM Models and Methods

Cited by 12 publications

References 102 publications

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Noise Invariant Convolution Neural Network for Segmentation of Multiple Sclerosis Lesions from Brain Magnetic Resonance Imaging

Multiple Sclerosis Disease Evolution Assessment in Brain MRI Lesions Based on Texture and Multi-Scale Amplitude Modulation-Frequency Modulation (AM-FM) Features

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