Image quality enhancement in wireless capsule endoscopy with Adaptive Fraction Gamma Transformation and Unsharp Masking filter

Ezatian, Rezvan; Khaledyan, Donya; Jafari, Kian; Heidari, Morteza; Khuzani, Abolfazl Zargari; Mashhadi, Najmeh

doi:10.1109/ghtc46280.2020.9342851

Cited by 8 publications

(6 citation statements)

References 27 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ultrasound images can suffer from various artifacts, such as speckle noise, attenuation, and poor contrast, which can affect the quality of the image and make it difficult to interpret. Image enhancement techniques can be applied to remove or reduce these artifacts and improve the overall quality of the image [42,43]. DL models can learn to identify and classify features in images based on patterns in the pixel values.…”

Section: Data Enhancement and Augmentationmentioning

confidence: 99%

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Khaledyan,

Marini,

O’Connell

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Objective.Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Approach. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks (CNNs), it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance.Main results.Two datasets are utilized for the analysis. The public "Breast Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818 images. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Significance. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

show abstract

Section: Data Enhancement and Augmentationmentioning

confidence: 99%

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Khaledyan,

Marini,

O’Connell

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Contrast limited adaptive histogram equalization (CLAHE) [24] is an image enhancement technique that improves contrast locally in small regions of an image, known as tiles. It avoids over-enhancement by limiting the modification [25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Developing an enhanced UNet-based architecture for breast tumor segmentation in ultrasound images

Khaledyan,

Marini,

O’Connell

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

View full text Add to dashboard Cite

Ultrasound imaging is a powerful imaging modality for diagnosing breast tumors due to its non-invasive nature, realtime imaging capabilities, and lack of ionizing radiation. Ultrasound imaging has certain limitations that can make it demanding to detect masses compared to other imaging modalities. Therefore, breast ultrasound image segmentation is a crucial and challenging task in computer-aided diagnosis (CAD) systems. Deep learning (DL) has revolutionized medical image segmentation. Among DL models, UNet architecture is widely used for its exceptional performance. This study assesses the effectiveness of sharpening filters and attention mechanisms between the decoder and encoder in UNet models for breast ultrasound segmentation. Combining Sharp UNet and Attention UNet, we propose a novel approach called Parallel Sharp Attention UNet (PSA_UNet). A public dataset of 780 cases was utilized in this study. The results are promising for the proposed method, with the Dice coefficient and F1 score of 0.93 and 0.94, respectively. McNemar's results show that our proposed model outperforms the earlier designs upon which our model is based. In addition to introducing a new network, this study highlights the importance of optimization and finetuning in improving UNet-based segmentation models. The results offer potential improvements in breast cancer diagnosis and treatment planning through more accurate and efficient medical image segmentation techniques.

show abstract

“…If necessary, the images must be put into video format, and finally, clinicians can see the images or video and study various causes of GI tract disorders. The capsule endoscopy is supposed to normally exit the body after around 24 hours (Khan et al, 2015) (Ezatian et al, 2020).…”

Section: Parts and Concept Of An Endoscopic Capsule Devicementioning

confidence: 99%

A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection

Sheet,

Farej,

Sheet

2024

EJCSIT

View full text Add to dashboard Cite

Wireless capsule endoscopy (WCE) is the gold standard for diagnosing small bowel disorders and is considered the future of effective diagnostic gastrointestinal (GI) endoscopy. Patients find it comfortable and more likely to adopt it than traditional colonoscopy and gastroscopy, making it a viable option for detecting cancer or ulcerations. WCE can obtain images of the GI tract from the inside, but pinpointing the disease's location remains a challenge. This paper reviews studies on endoscopy capsule development and discusses techniques and solutions for higher efficiency. Research has demonstrated that artificial intelligence (AI) enhances the accuracy of disease detection and minimizes errors resulting from physicians' fatigue or lack of attention. When it comes to WCE, deep learning has shown remarkable success in detecting a wide variety of disorders.

show abstract

Image quality enhancement in wireless capsule endoscopy with Adaptive Fraction Gamma Transformation and Unsharp Masking filter

Cited by 8 publications

References 27 publications

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Developing an enhanced UNet-based architecture for breast tumor segmentation in ultrasound images

A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection

Contact Info

Product

Resources

About