KEYWORDS: Endoscopy, RGB color model, Education and training, Diseases and disorders, Light sources and illumination, Color, Polyps, Image processing, Image analysis, Deep learning
Wireless capsule endoscopy (WCE) offers a minimally invasive approach to inspecting the gastrointestinal (GI) tract, crucial for diagnosing conditions such as malnutrition, dehydration, and potential cancers. However, WCE image diagnostics can be compromised by inadequate illumination and adversarial contrast reduction attacks. Adversarial contrast reduction attacks are intentional efforts to degrade image contrast and mislead automated diagnosis systems. Such challenges can result in misclassifications, negatively impacting patient safety. This study examines the effects of contrast degradation on Deep Learning (DL) models designed for WCE image analysis. The study emphasizes the adverse impact of substantial contrast reductions from adversarial attacks on classification accuracy. We propose a novel texture descriptor to mitigate this vulnerability: the Color Quaternion Modulus and Phase Patterns (CQ-MPP). This descriptor effectively extracts textural features within WCE images, enabling the identification of potentially cancerous regions, even under significantly reduced contrast. The effectiveness of CQ-MPP is evaluated using the Wireless Capsule Endoscopy Curated Colon Disease Dataset. Results show that CQ-MPP maintains good accuracy in detecting cancerous lesions and demonstrates remarkable resilience to contrast adversarial degradation. This method ensures reliable performance amidst severe contrast reduction, offering significant potential to improve safety of GI disease diagnosis via WCE.
Age-related macular degeneration (AMD) and Diabetic macular edema (DME) develop from irregularities in a section of the retina, causing vision impairment. Optical Coherence Tomography (OCT) imaging serves as the standard for identification, classification, and diagnosis of AMD and DME, determining locations of normal and irregular vascular patterns. However, challenges arise when OCT images are compromised by projection and motion artifacts concealing small lesions. This paper aims to develop an automated system for quantifying and categorizing AMD and DME (diabetic macula edema). The proposed approach, Multi-Kernel Wiener Local Binary Patterns (MKW-LBP) uses kernels of various sizes for feature extractions. Our proposed method is twofold: (1) Wiener patterns extract retinal features, robust against motion artifacts, thus preserving lesion visibility, and (2) multi-kernel vectorization exploits textural feature. Computer simulations demonstrate that the proposed technique achieves an overall accuracy of 98% through ten-fold cross-validation on the Duke University dataset. Furthermore, our system exhibits strong resistance against added Gaussian Noise, ensuring reliable performance under severe noise.
Skin cancer is the most common type of cancer in United States with 9,500 new cases diagnosed daily. It is one of the deadliest forms, however early detection and treatments can lead to recovery. More and more modern medical systems employs deep learning (DL) vision models as an assistive secondary diagnostic tool. This progress is derived from the superior performance by convolutional neural networks (CNNs) across a wide number of medical applications. However, recent discovery has revealed that adding small but faint noises to images can cause these models to make classification errors. These adversarial attacks can undermine defense measures and hamper the operations of deep learning models in real-world settings. The objective of this paper is to explore the effects of image degradation on popular off-the-shelf Deep Learning (DL) vision models. First, the investigation of the effects of adversarial attacks on image classification accuracy, sensitivity, and specificity are evaluated. Then we introduce pepper noise as an adversarial attack, which is an extension of the one-pixel attack on deep learning models. Second, we propose a novel texture descriptor Ordered statistics Local Binary Patterns (OS-LBP) for recognizing potential skin cancer areas. Third, we will demonstrate how our OS-LBP is successful in mitigating some of the effects of image degradations caused by adversarial attacks.
Spectral Domain Optical Coherence Tomography (SD-OCT) is a widely used imaging technique in ophthalmology. However, it often suffers from severe distortion due to speckle noise, which can obscure critical retinal structures and lesions. These distortions can significantly reduce accuracy of image-based diagnostic tasks. Developing effective techniques for reducing speckle noise and improving the quality of SD-OCT images is crucial. However, there are two main challenges in removing speckle noise: (1) balancing the removal of noise while preserving essential image details, and (2) that speckle noise can have varying intensity and size levels, making it challenging to develop a onesize-fits-all approach. If too much noise is removed, the image may become overly smoothed and lose essential details. On the other hand, if noises are not sufficiently removed, the image may still appear noisy and distorted. Different methods and algorithms may need to be used depending on the noise characteristics and the specific image being processed. Despite these challenges, various denoising techniques, such as wavelet-based, non-local means, and adaptive median filtering, have been proposed in the literature. Each method has its strengths and weaknesses, and the choice of the method should be based on the noise characteristics and the desired trade-off between noise removal and image preservation. While recent works in deep learning have shown promise in denoising OCT images, they require extensive training data and complex hardware, limiting their practicality in many settings. This paper presents an edge-preserving noise removal method for improving the quality of SD-OCT by reducing the effect of noise using a new morphology-based bitonic filter. This filter is created by combining extended Okada with various kernel sizes. This approach allows us to efficiently remove speckle noise from OCT images while minimizing the loss of details and enhancing image quality. Compared to existing methods, the presented approach is more efficient and requires fewer computational resources. It could enhance the accuracy of image-based diagnostic tasks, ultimately benefiting patients and clinicians alike.
Accurate ocular disorder classification and estimation of cornea depth and morphological changes depends on clear imaging of the affected structures. Ophthalmologists typically employ Optical Coherence Tomography (OCT) to help diagnose these conditions. This paper presents a new method called Alpha Mean Trim Local Binary Pattern (AMT-LPB) for automated texture classification of specific macular disease detected on OCT images of the retinal membrane. The performance of the proposed method achieved an overall accuracy of 99% using 10-fold cross-validation on the Duke University dataset [9].
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