Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

Tan, Tao; Mordang, Jan-Jurre; Zelst, Jan van; Grivegnee, André-Robert; Gubern-Mérida, Albert; Melendez, Jaime; Mann, Ritse M.; Zhang, Wei; Platel, Bram; Karssemeijer, Nico

doi:10.1118/1.4914162

Cited by 36 publications

(26 citation statements)

References 19 publications

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“…Afterwards, a thresholding operation is performed on this probabilistic image and the resulting local maximas are given to the next module wherein an accurate 3-D segmentation is done using a dynamic programming method. In (Tan et al 2015), Tan et al used a relatively similar approach for detecting small tumors. They used Haar-like features beside the mentioned seven features to improve the detection rate of small tumors.…”

Section: Clusteringmentioning

confidence: 99%

“…In addition to the mentioned features, Tan et al used coronal spiculation, gradient concentration, Haar, Gabor and GLCM features by considering different window sizes to detect small tumors (Tan et al 2015). Based on their evaluation, using these features improves the sensitivity of CADe system for recognition of small masses whose radius is ranged between 1 and 5 mm.…”

Section: Texture Featuresmentioning

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: Clusteringmentioning

confidence: 99%

Section: Texture 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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“…Therefore, 3D segmentation methods are desirable for more accurate segmentation in volumetric imagery. Some approaches have been proposed on different modalities such as breast MRI (Song et al, 2015[124]) and automated 3D breast ultrasound (Moon et al, 2013[98]; Tan et al, 2015[129], 2013[130]).…”

Section: Cornerstones Of a Cad Systemmentioning

confidence: 99%

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian

Mashohor

Mahmud

et al. 2017

EXCLI Journal; 16:Doc113; ISSN 1611-2156

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“…The demand to develop segmentation technique in 3D space has increased recently. Some approaches have been proposed on different modalities such as breast MRI [1], automated 3D breast ultrasound [2][3][4] and mammography [5,6]. Development of reconstruction algorithms has been the attention of many researchers in latest work since reconstruction of 3D images of consecutive images has various applications in surgery, teaching anatomy and diagnosis [7].…”

Section: Introductionmentioning

confidence: 99%

3D reconstruction for volume of interest in computed tomography laser mammography images

Jalalian

Mashohor

Mahmud

et al. 2015

2015 IEEE Student Symposium in Biomedical Engineering &Amp; Sciences (ISSBES)

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Computer assisted diagnosis systems (CADs) is now commonly used as a second opinion to help radiologists in image interpretation by emphasizing on the suspicious areas. Segmentation of region of interests in 2-dimensional (2D) or volume of interests in 3-dimensional (3D) images is a critical step in CAD systems. 3D image segmentation using 2D slices has been a keen of interest for research purpose. In this paper we propose to reconstruct a 3D form of volume of interests (VOIs) from a series of 2D images in computed tomography laser mammography (CTLM). In this paper, a 3D Fuzzy C-Means clustering have been implemented to reconstruct VOIs for breast cancer detection in CTLM images.To assess the accuracy of the extracted VOIs against ground truth, percentage error factor is used and produced error value of 10.72% in our dataset of 62 CTLM breast images collected among Malaysian participants.

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Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

Abstract: The proposed technique significantly improves our previously developed CADe system in the initial candidate detection stage.

Cited by 36 publications

References 19 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

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

3D reconstruction for volume of interest in computed tomography laser mammography images

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