A saliency model for automated tumor detection in breast ultrasound images

Shao, Haoyang; Zhang, Yingtao; Xian, Min; Cheng, Heng-Da; Xu, Fei; Ding, Jianrui

doi:10.1109/icip.2015.7351035

Cited by 32 publications

(60 citation statements)

References 14 publications

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“…In Figure 10(c), although the tumor boundary is quite clear, IGAC still achieves wrong segmentation due to leakage. The proposed method can obtain accurate results as shown in For evaluating segmentation results, three area error metrics were used: the true positive (TP) ratio, the false positive (FP) ratio, and the similarity (SI) [30,31]. They are popularly used for evaluating the performance of segmentation.…”

Section: Resultsmentioning

confidence: 99%

A Novel Fuzzy Level Set Approach for Image Contour Detection

Zhang¹,

Xu²,

Cheng³

2016

Mathematical Problems in Engineering

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The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

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Section: Resultsmentioning

confidence: 99%

A Novel Fuzzy Level Set Approach for Image Contour Detection

Zhang¹,

Xu²,

Cheng³

2016

Mathematical Problems in Engineering

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“…On the other hand, the work in Reference [8] presents a sigmoid model based on a cross-correlation algorithm to solve estimation errors in ultrasound images. More recently, saliency models and convolutional neural networks (CNNs) have been used for automatic tumor detection and breast segmentation and in ultrasound images [9,10].…”

Section: Introductionmentioning

confidence: 99%

A q-Extension of Sigmoid Functions and the Application for Enhancement of Ultrasound Images

Rodrigues

Wachs-Lopes

Santos

et al. 2019

Entropy

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This paper proposes the q-sigmoid functions, which are variations of the sigmoid expressions and an analysis of their application to the process of enhancing regions of interest in digital images. These new functions are based on the non-extensive Tsallis statistics, arising in the field of statistical mechanics through the use of q-exponential functions. The potential of q-sigmoids for image processing is demonstrated in tasks of region enhancement in ultrasound images which are highly affected by speckle noise. Before demonstrating the results in real images, we study the asymptotic behavior of these functions and the effect of the obtained expressions when processing synthetic images. In both experiments, the q-sigmoids overcame the original sigmoid functions, as well as two other well-known methods for the enhancement of regions of interest: slicing and histogram equalization. These results show that q-sigmoids can be used as a preprocessing step in pipelines including segmentation as demonstrated for the Otsu algorithm and deep learning approaches for further feature extractions and analyses.

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“…Many automatic BUS segmentation approaches have been proposed in the last decade [3,18,21,[33][34][35][36]. The major strategy of the approaches is to locate tumors automatically by modeling domain-related priors.…”

Section: Introductionmentioning

confidence: 99%

“…The TSE outputs the saliency value of each BUS image pixel in terms of the pixel's possibility of belonging to a tumor. In [21] saliency estimation hypothesis and achieved very good performance using their BUS image dataset. However, it has two main drawbacks: 1) it always outputs a salient region and cannot deal with images without tumor; 2) the predefined mapping failed to handle the images with large tumors, shadows, and low contrast ( Fig.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Tumor Saliency Estimation

Xian

Zhang

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality. Most tumor segmentation approaches achieve good performance on BUS images collected in controlled settings; however, the performance degrades greatly with BUS images from different sources. Tumor saliency estimation (TSE) has attracted increasing attention to solve the problem by modeling radiologists' attention mechanism. In this paper, we propose a novel hybrid framework for TSE, which integrates both high-level domain-knowledge and robust low-level saliency assumptions and can overcome drawbacks caused by direct mapping in traditional TSE approaches. The new framework integrated the Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the layer structure-based weighted map. The experimental results demonstrate that the proposed approach outperforms state-of-the-art TSE methods.

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A saliency model for automated tumor detection in breast ultrasound images

Cited by 32 publications

References 14 publications

A Novel Fuzzy Level Set Approach for Image Contour Detection

A Novel Fuzzy Level Set Approach for Image Contour Detection

A q-Extension of Sigmoid Functions and the Application for Enhancement of Ultrasound Images

A Hybrid Framework for Tumor Saliency Estimation

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