Proceedings ArticleDOI
Object Boundary Detection in Ultrasound Images
Moi Hoon Yap,Eran A. Edirisinghe,Helmut E. Bez +2 more
- pp 53-53
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TLDR
This paper presents a novel approach to boundary detection of regions-of-interest (ROI) in ultrasound images, more specifically applied to ultrasound breast images, and compares the performance of the algorithm with two well known methods.Abstract:
This paper presents a novel approach to boundary detection of regions-of-interest (ROI) in ultrasound images, more specifically applied to ultrasound breast images. In the proposed method, histogram equalization is used to preprocess the ultrasound images followed by a hybrid filtering stage that consists of a combination of a nonlinear diffusion filter and a linear filter. Subsequently the multifractal dimension is used to analyse the visually distinct areas of the ultrasound image. Finally, using different threshold values, region growing segmentation is used to the partition the image. The partition with the highest Radial Gradient Index (RGI) is selected as the lesion. A total of 200 images have been used in the analysis of the presented results. We compare the performance of our algorithm with two well known methods proposed by Kupinski et al. and Joo et al. We show that the proposed method performs better in solving the boundary detection problem in ultrasound images.read more
Citations
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Journal ArticleDOI
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
Moi Hoon Yap,Gerard Pons,Joan Martí,Sergi Ganau,Melcior Sentís,Reyer Zwiggelaar,Adrian K. Davison,Robert Martí +7 more
TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.
Journal ArticleDOI
A novel algorithm for initial lesion detection in ultrasound breast images.
TL;DR: The proposed method is more accurate and performs more effectively than do the benchmark algorithms considered and compared that of three state‐of‐the‐art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique.
Journal ArticleDOI
Breast ultrasound lesions recognition:: end-to-end deep learning approaches
Moi Hoon Yap,Manu Goyal,Fatima M. Osman,Robert Martí,Erika R. E. Denton,Arne Juette,Reyer Zwiggelaar +6 more
TL;DR: This work proposes the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet,FCN-32s, FCn-16s, and FCN -8s for semantic segmentation of breast lesions and shows that the proposed method performed better on benign lesions.
Journal ArticleDOI
Breast ultrasound region of interest detection and lesion localisation.
Moi Hoon Yap,Manu Goyal,Fatima M. Osman,Robert Martí,Erika R. E. Denton,Arne Juette,Reyer Zwiggelaar +6 more
TL;DR: This work proposes the use of a deep learning method for breast ultrasound ROI detection and lesion localisation and uses the most accurate object detection deep learning framework - Faster-RCNN with Inception-ResNet-v2 - as the deep learning network.
Journal ArticleDOI
Despeckling of ultrasound images of bone fracture using multiple filtering algorithms
TL;DR: The results of the study carried out to reduce speckle using filtering algorithms such as Wiener, Average, Median, Anisotropic Diffusion and Wavelets suggest that the combination of Daubechies–Wiener, which is a hybrid technique with Anisotrop Diffusion, gave the best performance.
References
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American Cancer Society Guidelines for Breast Cancer Screening: Update 2003
Robert A. Smith,Debbie Saslow,Kimberly Andrews Sawyer,Wylie Burke,Mary E. Costanza,W. Phil Evans,Roger S. Foster,Edward Hendrick,Harmon J. Eyre,Steven Sener +9 more
TL;DR: The new screening recommendations address screening mammography, physical examination, screening older women and women with comorbid conditions, screening women at high risk, and new screening technologies.
Journal ArticleDOI
Texture segmentation using fractal dimension
Bidyut B. Chaudhuri,N. Sarkar +1 more
TL;DR: A modified box-counting approach is proposed to estimate the FD, in combination with feature smoothing in order to reduce spurious regions and to segment a scene into the desired number of classes, an unsupervised K-means like clustering approach is used.