Tumor detection in automated breast ultrasound images using quantitative tissue clustering

Moon, Woo Kyung; Lo, Chung-Ming; Chen, Rong Tai; Shen, Yi; Chang, Jung Min; Huang, Chiun‐Sheng; Chen, Jeon Hor; Hsu, Wei-Feng; Chang, Rong‐Yeu

doi:10.1118/1.4869264

Cited by 58 publications

(63 citation statements)

References 37 publications

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“…On the other hand, if k is taken small then the denoising effect is not considerable. Moon et al used image clustering to remove speckle noise (Moon et al 2014). They applied mean shift to each voxel separately to partition the input image.…”

Section: Despecklingmentioning

confidence: 99%

“…Edge enhancement is another preprocessing operation which improves the performance of CADe systems. Only one method called stick filter (Awad J 2003) has been used to enhance lines in ABUS images (Chang et al 2010;Lo et al 2014a;Moon et al 2014). Sticks are line segments with different orientations which are applied as templates to the image.…”

Section: Edge Enhancementmentioning

confidence: 99%

“…State-of-the-art methods usually use a combination of texture, morphological and positional features to reduce the number of false positives (Kozegar et al 2017;Moon et al 2013Moon et al , 2014Tan et al 2013aTan et al , 2015. Table 5 summarizes these three types of features.…”

Section: Position Based Featuresmentioning

confidence: 99%

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Computer aided detection in automated 3-D breast ultrasound images: a survey

et al. 2019

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Nowadays, breast cancer is the leading cause of cancer death for women all over the world. Since the reason of breast cancer is unknown, early detection of the disease plays an important role in cancer control, saving lives and reducing costs. Among different modalities, automated 3-D breast ultrasound (3-D ABUS) is a new and effective imaging modality which has attracted a lot of interest as an adjunct to mammography for women with dense breasts. However, reading ABUS images is time consuming for radiologists and subtle abnormalities may be overlooked. Hence, computer aided detection (CADe) systems can be utilized as a second interpreter to assist radiologists to increase their screening speed and sensitivity. In this paper, a general architecture representing different CADe systems for ABUS images is introduced and the approaches for implementation of each block are categorized and reviewed. In addition, the limitations of these systems are discussed and their performance in terms of sensitivity and number of false positives per volume are compared.

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Section: Despecklingmentioning

confidence: 99%

Section: Edge Enhancementmentioning

confidence: 99%

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Computer aided detection in automated 3-D breast ultrasound images: a survey

et al. 2019

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“…Speckle suppression filter is expected to debase the influence of speckle noise and improve the segmentation accuracy of region of interest (ROI). To this end, we apply Fuzzy C Mean (FCM) algorithm [51] in segmentation of tumor regions of BUS images which are filtered by the algorithms investigated in this study and evaluate their performances by comparing the segmentation accuracies.…”

Section: Tumor Region Segmentation Accuracymentioning

confidence: 99%

“…In Experiment 4, the FCM algorithm [51] was utilized for BUS segmentation on the filtered images which have been filtered in Experiment 3. The ARE value can be computed so as to compare image segmentation accuracy.…”

Section: Description Of Experimentsmentioning

confidence: 99%

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

2016

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Abstract:Breast ultrasound is an important tool used in the medical treatment and diagnosis of breast tumor. However, noise defined as speckles are generated inevitably. Although the existence of speckle may be beneficial to diagnosis if used by a well-trained observer, it often causes disturbance which negatively affects clinical diagnosis, not only by reducing resolution and contrast of ultrasound images, but also by adding difficulties to recognize tumor region accurately. In this paper, we investigate a number of popular de-speckling algorithms, including filters based on frequency domain, filters based on local statistical properties, filters based on minimum mean square error (MMSE), and filters based on Partial Differential Equation (PDE). Two visual measurement evaluation criteria, i.e., Mean to Variance Ratio (VMR) and Laplace Response of Domain (LRD), are chosen for the performance comparison of those filters in the application of ultrasound breast image filtering. Moreover, the filtering effect is further evaluated with respect to the segmentation accuracy of tumor regions. According to the evaluation results, we conclude that Bilateral Filter (BF) achieves the best visual effect. Although Weickert J Diffusion (WJD) and Total Variation (TV) can also obtain good visual effect and segmentation accuracy, they are very time-consuming.

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A 2‐Phase Merge Filter Approach to Computer‐Aided Detection of Breast Tumors on 3‐Dimensional Ultrasound Imaging

Chiu

Kuo

Chen

et al. 2020

J of Ultrasound Medicine

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Objectives-The role of image analysis in 3-dimensional (3D) automated breast ultrasound (ABUS) images is increasingly important because of its widespread use as a screening tool in whole-breast examinations. However, reviewing a large number of images acquired from ABUS is time-consuming and sometimes error prone. The aim of this study, therefore, was to develop an efficient computeraided detection (CADe) algorithm to assist the review process. Methods-The proposed CADe algorithm consisted of 4 major steps. First, initial tumor candidates were formed by extracting and merging hypoechoic square cells on 2-dimensional (2D) transverse images. Second, a feature-based classifier was then constructed using 2D features to filter out nontumor candidates. Third, the remaining 2D candidates were merged longitudinally into 3D masses. Finally, a 3D feature-based classifier was used to further filter out nontumor masses to obtain the final detected masses. The proposed method was validated with 176 passes of breast images acquired by an Acuson S2000 automated breast volume scanner (Siemens Medical Solutions USA, Inc., Malvern, PA), including 44 normal passes and 132 abnormal passes containing 162 proven lesions (79 benign and 83 malignant). Results-The proposed CADe system could achieve overall sensitivity of 100% and 90% with 6.71 and 5.14 false-positives (FPs) per pass, respectively. Our results also showed that the average number of FPs per normal pass (7.16) was more than the number of FPs per abnormal pass (6.56) at 100% sensitivity. Conclusions-The proposed CADe system has a great potential for becoming a good companion tool with ABUS imaging by ensuring high sensitivity with a relatively small number of FPs.

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Tumor detection in automated breast ultrasound images using quantitative tissue clustering

Abstract: The proposed CADe system provides an automatic and quantitative procedure for tumor detection in ABUS images. Further studies are needed to reduce the FP rate of the CADe algorithm.

Cited by 58 publications

References 37 publications

Computer aided detection in automated 3-D breast ultrasound images: a survey

Computer aided detection in automated 3-D breast ultrasound images: a survey

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

A 2‐Phase Merge Filter Approach to Computer‐Aided Detection of Breast Tumors on 3‐Dimensional Ultrasound Imaging

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