Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation

Alemán‐Flores, Miguel; Álvarez, Luis; Caselles, Vicent

doi:10.1007/s10851-007-0015-8

Cited by 64 publications

(30 citation statements)

References 29 publications

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“…The preprocessing methods used-namely input image inversion, histogram equalization [1], anisotropic diffusion filtering (ADF) [47,48], Gaussian filtration-are to reduce noise, amplify uniform areas of the processed image and thus improve the operation of the active contour models, namely: EM and GAC. In the case of ACWE, it is sufficient to use only the Close-Open filtering (CO), which helps reduce noise.…”

Section: Methodsmentioning

confidence: 99%

“…A block diagram of the proposed method of segmenting suspicious-looking masses from mammograms using the following active contour models: an active contour without edges ACWE, an edge-based active contour model using an inflation/deflation force with a damping coefficient EM and a geometric active contour model GAC. [47,48]. To emphasise details better, the example is of the same height and width as the image before sampling, i.e., from letter (c); (f) EM model use: the rectangular seed contour and the final contour approximating the boundaries of the lesion; (g) EM model use: the final contour overlaid on the input image from letter (a) after the processed image has been restored to the size of 512 × 512 pixels; (h) GAC model use: the rectangular seed contour and the final contour approximating the boundaries of the lesion; (i) GAC model use: The final contour overlaid on the input image from letter (a) after the processed image has been restored to the size of 512 × 512 pixels; (j) the use of the ACWE model (the same preprocessing as in the EM and GAC models): a rectangular seed contour and the final contour; (k) ACWE: a rectangular seed contour and the final contour approximating the edges of the lesion in a source image after the CO filtration; (l) ACWE: the final contour overlaid on the input image from item (a).…”

Section: Methodsmentioning

confidence: 99%

“…According to [48], the anisotropic diffusion (I t ) of the image (I) is defined by the following equation:…”

Section: Anisotropic Diffusion Filtering (Adf)mentioning

confidence: 99%

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Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Ciecholewski

2017

Symmetry

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Abstract:The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to segment the masses: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), a geometric active contour model (GAC) and an active contour without edges (ACWE). The preprocessing techniques presented in this publication are to reduce noise and at the same time amplify uniform areas of images in order to improve segmentation results. In addition, the use of image sampling by bicubic interpolation was tested to shorten the evolution time of active contour methods. The experiments used a test set composed of 100 cases taken from two publicly available databases: Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) database. The qualitative assessment concerned the ability to formulate an adequate diagnostic hypothesis and, for the individual methods (malignant and benign cases together), it amounted to at least: 81% (EM), 76% (GAC), and 69% (ACWE).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Ciecholewski

2017

Symmetry

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“…Since an AC has higher curvature in the narrower area, we apply a curvature force fκ to overcome this. Similar to the applications of curvature in [9,4], fκ for each snaxel is defined as fκ(u…”

Section: Cellular Forcesmentioning

confidence: 99%

“…Of course a few alternative active models have introduced different modules to solve similar problems, such as statistical constraints [2], primitive shape priors [3] and texture [4]. These tools, however, are based in the image domain and do not incorporate shape information in a way that will overcome overlap problems.…”

Section: Introductionmentioning

confidence: 99%

Cellsnake: A new active contour technique for cell/fibre segmentation

Pan

Kokaram

Gilmore

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Active contours are well known for object segmentation and widely adopted in various forms for biological image analysis. Most of the techniques are commonly based on object geometry but overlapping regions cause severe problems to contour propagation. In this paper, we propose a novel active contour technique ("cellsnake") for solving this problem with an application to cell and fibre segmentation. Given that the transparency of overlapped objects is unavailable, we present a new set of contour forces derived from a-priori knowledge of cell geometry that allows the contour to deform correctly in those regions. We have combined these terms with other existing forces and we show that cellsnake gives appropriate shape estimation of the objects especially in the overlapped area in the observed images.

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Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

Lei

He²,

Yao

et al. 2020

Medical Physics

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Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer‐aided diagnosis (CAD). This work aims to develop a deep learning‐based method for breast tumor segmentation using three‐dimensional (3D) ABUS automatically. Methods For breast tumor segmentation in ABUS, we developed a Mask scoring region‐based convolutional neural network (R‐CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R‐CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross‐validation and 30 were used for hold‐out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland–Altman analysis. Results The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross‐validation and hold‐out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([−0.77, 1.71)) for the cross‐validation and 0.23 cc ([−0.23 0.69]) for hold‐out test, respectively. Conclusion We developed a novel Mask scoring R‐CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning‐based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

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Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation

Cited by 64 publications

References 29 publications

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Cellsnake: A new active contour technique for cell/fibre segmentation

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

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