Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching

Ertaş, Gökhan; Gülçür, Halil Özcan; Osman, Onur; Uçan, Osman N.; Tunacı, Mehtap; Dursun, Memduh

doi:10.1016/j.compbiomed.2007.08.001

Cited by 68 publications

(50 citation statements)

References 27 publications

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“…Ertas et al developed an automatic algorithm for the detection of breast lesions based on cellular neural network segmentation and 3D template matching (14). They assessed the performance of their system on a dataset of 39 lesions, of which 19 were benign and 20 malignant.…”

Section: Discussionmentioning

confidence: 99%

“…As it is not operator dependent, a fully automatic lesion segmentation process has the potential to reduce reading time and provide more reproducible results. Unfortunately, few studies have addressed automatic lesion detection and segmentation techniques for breast DCE-MRI (14)(15)(16). Furthermore, to our knowledge these methods have been tested only on non-fat-saturated (fat-sat) contrast-enhanced images.…”

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confidence: 99%

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Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI.Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrastenhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (systemdetected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). Conclusion:The system showed promising results on MR datasets obtained from different scanners producing fatsat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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“…Some studies proposed automated approaches for breast segmentation. Ertas et al [7] presented a breast segmentation method using two kinds of cellular neural networks; one is for thresholding and another for removing small objects and smoothing sharp corners. In another work [8], 3D bias-corrected fuzzy c-means clustering and morphological operations were used to determine the breast region.…”

Section: Introductionmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast-chest wall boundary. Finally, the obtained breast-chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

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“…The number of pixels of ( ) and ( ) have to be found first, where ( ) represents the segmented region by the proposed approach, while ( ) represents the ground truth regions segmented by the experts. The evaluation measures used in this study are; True Positive Fraction (TPF) (also called Sensitivity), True Negative Fraction (TNF) [23][24][25][26], Relative Overlap (RO) (also called segmentation precision) and Misclassification Rate (MCR) which have been used before for brain segmentation [27] and in breast segmentation [8,28]. The calculations are made using the equations 5-8.…”

Section: Resultsmentioning

confidence: 99%

Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering

Al-Faris

Ngah

Isa

et al. 2013

Advances in Intelligent Systems and Computing

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Abstract. In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. The paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. The PSO clusters' intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological Thinning. The system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. The results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset.

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Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching

Cited by 68 publications

References 27 publications

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Breast-region segmentation in MRI using chest region atlas and SVM

Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering

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