Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Piantadosi, Gabriele; Marrone, Stefano; Fusco, Roberta; Sansone, Mario

doi:10.1049/iet-cvi.2018.5273

Cited by 22 publications

(8 citation statements)

References 103 publications

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“…The maximum SPE achieved in the proposed study is 67.00%. The SPE value of the classifier is equal to that of the study given in reference [75] and higher than the percentage obtained in reference [32]. The FP (identifying a benign lesion as malignant) error is not extremely important in terms of health but sometimes cause despondency of the patient.…”

Section: Discussionmentioning

confidence: 74%

“…Then the local adaptive thresholding process is applied to the images to dominate the intensity inhomogeneity raised from the bias field and low contrast intensity on the gyrate region between breast and pectoral muscle. Although there exist a few thresholding methods, Niblack's technique is preferred in this study according to the performed several trials on the database [76]. The denoised and thresholded image is shown in Fig.…”

Section: Breast Region Segmentationmentioning

confidence: 99%

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Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging

2020

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The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k-nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.

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Section: Discussionmentioning

confidence: 74%

Section: Breast Region Segmentationmentioning

confidence: 99%

Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging

2020

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“…Unfortunately, choosing the most-appropriate MCT is not straightforward since we proved that there is not a single motion-correction technique always performing better than the others when applied to distinct patients or to distinct DCE-MRI protocols [43]. Nonetheless, and even though the proposed approach can be used with any MCT, in this work, we make use of a 3D non-rigid intensity-based registration provided by Elastix [41], an open-source software collecting image-registration techniques for medical images, as it showed to be among the most effective [7].…”

Section: Motion Correctionmentioning

confidence: 99%

“…CAD systems consist of several modules, each intended to perform a given task. In the case of breast DCE-MRI, one of the hardest tasks is the lesion segmentation, namely, the pixel-wise identification of a suspected region of interest (ROI) [7]. Indeed, with the spread of high-precision tasks, such as MRI-guided robotic surgery [8], neoadjuvant chemoradiation [9], etc., the coarse lesion detection is no longer sufficient (see Section 2).…”

Section: Introductionmentioning

confidence: 99%

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

et al. 2021

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The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability.

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“…The obtained results are 0.90, 0.82, 0.89, 0.1 and 0.09, respectively [5]. In [6], breast lesion automatic detection and diagnosis system (BLADes) is introduced to support the radiologist during the breast cancer diagnosis. The performance of the system is evaluated on histopathologically proven lesions and promising results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

Çetinel

Mutlu

Gül

2020

International Journal of Applied Mathematics Electronics and Computers

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Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.

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Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Cited by 22 publications

References 103 publications

Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging

Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

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