Rapid image stitching and computer‐aided detection for multipass automated breast ultrasound

Chang, Ruey-Feng; Chang-Chien, Kuang-Che; Takada, Etsuo; Huang, Chiun‐Sheng; Chou, Yi‐Hong; Kuo, Chen-Ming; Chen, Jeon‐Hor

doi:10.1118/1.3377775

Cited by 36 publications

(30 citation statements)

References 45 publications

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“…The pixel intensity was used in most of the previous CAD systems [4,25]. We found that the results of the proposed CAD system were similar to those of a previous CAD system using a blob detection algorithm [23].…”

Section: Discussionmentioning

confidence: 62%

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

et al. 2014

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Purpose:The aim of this study was to evaluate the performance of a proposed computer-aided detection (CAD) system in automated breast ultrasonography (ABUS).Methods:Eighty-nine two-dimensional images (20 cysts, 42 benign lesions, and 27 malignant lesions) were obtained from 47 patients who underwent ABUS (ACUSON S2000). After boundary detection and removal, we detected mass candidates by using the proposed adjusted Otsu's threshold; the threshold was adaptive to the variations of pixel intensities in an image. Then, the detected candidates were segmented. Features of the segmented objects were extracted and used for training/testing in the classification. In our study, a support vector machine classifier was adopted. Eighteen features were used to determine whether the candidates were true lesions or not. A five-fold cross validation was repeated 20 times for the performance evaluation. The sensitivity and the false positive rate per image were calculated, and the classification accuracy was evaluated for each feature.Results:In the classification step, the sensitivity of the proposed CAD system was 82.67% (SD, 0.02%). The false positive rate was 0.26 per image. In the detection/segmentation step, the sensitivities for benign and malignant mass detection were 90.47% (38/42) and 92.59% (25/27), respectively. In the five-fold cross-validation, the standard deviation of pixel intensities for the mass candidates was the most frequently selected feature, followed by the vertical position of the centroids. In the univariate analysis, each feature had 50% or higher accuracy.Conclusion:The proposed CAD system can be used for lesion detection in ABUS and may be useful in improving the screening efficiency.

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Section: Discussionmentioning

confidence: 62%

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

et al. 2014

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“…breast tumor, prostate cancer, obstetrics) using conventional ultrasound have been well studied, as surveyed in [29], [30]. From the computational point, various methods [31], [32] were proposed to segment the suspicious lesions by using the intensity or edge information. Hessian analysis were also explored to segment common geometrical structures for all kinds of tumors [33]- [35] with different modalities (e.g.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

Liang

Cao

Huang

et al. 2016

IEEE Trans. Med. Imaging

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This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http://vision.sysu.edu.cn/projects/fllrecog/".

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“…Image stitching technologies consist of two steps, image registration and image blending [10][11][12]. The image registration can be classified into rigid registration and nonrigid registration.…”

Section: Introductionmentioning

confidence: 99%

Image Stitching for Achilles Tendon Ultrasonography Using Particle Swarm Optimization of Boundary Vector Field Based Similarity and Log-Domain Diffeomorphic Registration

Xiong¹,

Zhang²,

Suo³

et al. 2017

J Med Imaging Case Rep

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Rapid image stitching and computer‐aided detection for multipass automated breast ultrasound

Abstract: The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD-based registration in order to detect tumors.

Cited by 36 publications

References 45 publications

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

Image Stitching for Achilles Tendon Ultrasonography Using Particle Swarm Optimization of Boundary Vector Field Based Similarity and Log-Domain Diffeomorphic Registration

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