Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

Liu, Bo; Cheng, Heng-Da; Huang, Jianhua; Tian, Jiawei; Tang, Xianglong; Liu, Jiafeng

doi:10.1016/j.patcog.2009.06.002

Cited by 139 publications

(101 citation statements)

References 40 publications

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“…From Table 2, we can see that, in most cases, the method proposed by this paper has better performance than that of [14]. And the best performance is reached when 49 neurons were employed by SOM.…”

Section: Resultsmentioning

confidence: 85%

“…This confirms that the local features can be used to classify the tumors into benign and malignant well. When local features are modeled and extracted, the problem is more suitable for utilizing MIL than utilizing traditional supervised learning as described in [14]. The receiver operator characteristic (ROC) curves are also utilized to evaluate the performance of the proposed method as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The global features of lesions such as shape and margin can be extracted from Fig. 4 The ROC curves of the proposed method and the one in [14] Fig. 5 The ROC curves of the proposed method, Citation-KNN, and EM-DD segmented results.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous work [14], the image is enhanced in fuzzy domain [23]. The S-function is used as the fuzzy membership function [24].…”

Section: Previous Work Reviewmentioning

confidence: 99%

“…In method of [14], the local texture features of the subregions in roughly segmented ROIs were used to characterize lesions. The experiments demonstrated that such strategy was robust to segmentation result.…”

Section: Introductionmentioning

confidence: 99%

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Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

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Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p<0.005).

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…In our previous work [14], the image is enhanced in fuzzy domain [23]. The S-function is used as the fuzzy membership function [24].…”

Section: Previous Work Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

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Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System

Guo

Wang

et al. 2017

J of Ultrasound Medicine

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Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

O’Connell

Bartolotta

Orlando

et al. 2021

J of Ultrasound Medicine

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Objectives We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. Methods A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. Results The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI‐RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies. Conclusion The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.

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Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

Cited by 139 publications

References 40 publications

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System

Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

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