Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience

Ikedo, Yuji; Morita, Takako; Fukuoka, Daisuke; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko

doi:10.1007/s11548-009-0295-0

Cited by 11 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ABVS was significantly more effective than conventional ultrasonography in detecting retraction phenomena in coronal sections among 17 patients, a finding that is in line with previous studies [11,12]. This increased detection rate could be attributed to the fact that ABVS scans a greater breast tissue volume, thus providing more diagnostic information [13]. Nevertheless, the number of patients with malignancies in the present study was too limited to firmly confirm this useful aspect of ABVS.…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

The efficacy of automated breast volume scanning over conventional ultrasonography among patients with breast lesions

Xiao

Chen

Zhou

et al. 2015

Intl J Gynecology & Obste

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 91%

“…All ultrasonography examinations were performed by licensed physicians with at least 5 years of experience, using the ACUSON S2000 system with an integrated linear transducer (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)…”

Section: Methodsmentioning

confidence: 99%

The efficacy of automated breast volume scanning over conventional ultrasonography among patients with breast lesions

Xiao

Chen

Zhou

et al. 2015

Intl J Gynecology & Obste

View full text Add to dashboard Cite

show abstract

“…The classification scheme resulted in the accuracy of 83.3% (10/12cases) in mottled pattern cases. Ruey-Feng and Wen-Jie [19] worked on segmenting the tumors in ultrasound images using the newly developed level set method at first and then six morphologic features are used to distinguish the benign and malignant cases. In the experiment, the accuracy of SVM with shape information for classifying malignancies was 90.95% (191/210) and the sensitivity was seen to be 88.89% (80/90).…”

Section: Literaturementioning

confidence: 99%

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Minavathi¹,

Murali²,

Dinesh³

2012

IJCA

View full text Add to dashboard Cite

This work presents a new approach for classifying masses in breast ultrasound images. Detection and classification of masses in ultrasound images still remains a challenge because most of the ultrasound images contain speckle noise and fuzzy boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it increases the rate of detection in dense breasts. Ultrasound also does dynamic analysis of moving structures in breast thus it is used to analyze the functional behavior of breast. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used: the structure of mass in accordance with spiculations, elliptical shape of the mass and acoustic shadowing feature which is an important functional feature. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 92.7% of sensitivity with 0.88 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected and classified mass ROI candidates in breast ultrasound images.

show abstract

“…Therefore, screening and early diagnosis of breast cancer are very necessary. At this stage, the main methods to diagnose breast cancer are MRI, PET, ultrasound imaging and X-ray, Compared with MRI, PET and X-ray, ultrasound imaging technology is not only inexpensive and non-radioactive, but also can show most of the lesion areas in each section, especially for more compact breast tissues of young Asian women [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Method for Localization and Classification of Breast Ultrasound Tumors

Zhu

Wang

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Ultrasound instruments are suitable for large-scale examination of breast tumors, especially for women from Asian whose glands are dense. However, ultrasound images have the low contrast and resolution, blurred boundary and artifacts, which bring great difficulties to the interpretation of the junior doctor. However, traditional methods of breast ultrasound tumor recognition often use manually extracted features to gradually realize ROI region location and tumor classification with low accuracy, poor robustness and weak universality. Deep learning is limited to the location of tumor ROI region or the classification of a given tumor ROI region. In this paper, YOLOV3 algorithm is used for breast ultrasound tumor recognition, which could realize ROI localization and tumor classification at the same time. In addition, K-Means is optimized by K-Means++ and K-Mediods algorithm to generate anchor boxes of YOLOV3, and based on the Darknet-53 network structure of YOLOV3, ResNet and DenseNet are combined to design ResNet-DenseNet_Darknet-53. The proposed method is tested on the breast ultrasound tumor data set. Experiments show that the improved YOLOV3 algorithm shows better detection results on multiple evaluation indicators.

show abstract

Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience

Abstract: The feasibility of an automated ultrasonography classifier for parenchymal patterns was demonstrated with promising results in whole breast US images.

Cited by 11 publications

References 27 publications

The efficacy of automated breast volume scanning over conventional ultrasonography among patients with breast lesions

The efficacy of automated breast volume scanning over conventional ultrasonography among patients with breast lesions

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

A Method for Localization and Classification of Breast Ultrasound Tumors

Contact Info

Product

Resources

About