Automatic pectoral muscle removal in mammograms

Kebede, Samuel Rahimeto; Debelee, Taye Girma; Yohannes, Dereje; Schwenker, Friedhelm

doi:10.1007/s12530-019-09310-8

Cited by 28 publications

(22 citation statements)

References 5 publications

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“…Rahimeto et. al proposed an automatic pectoral muscle removal technique for breast mammograms 10 . They used wiener filtering for noise removal and Otsu's algorithm for extraction of the biggest blob.…”

Section: Related Workmentioning

confidence: 99%

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

Ali

Raza

Mahmood

et al. 2020

Int J Imaging Syst Tech

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The precise detection and segmentation of pectoral muscle areas in mediolateral oblique (MLO) views is an essential step in the development of a computer‐aided diagnosis system to access breast malignant lesions or parenchyma. The goal of this article is to develop a robust and fully automatic algorithm for pectoral muscle segmentation from mammography images. This paper presents an image enhancement approach that improves the quality of mammogram scans and a convolutional neural network‐based fully convolutional network architecture enhanced with residual connections for automatic segmentation of the pectoral muscle from the MLO views of a digital mammogram. For this purpose, the model is tested and trained on three different mammogram datasets named MIAS, INBREAST, and DDSM. The ground truth labels of the pectoral muscle were identified under the supervision of experienced radiologists. For training and testing, 10‐fold cross‐validation was used. The proposed model was compared with baseline U‐Net‐based architecture. Finally, we used a postprocessing step to find the actual boundary of the pectoral muscle. Our presented architecture generated a mean Intersection over Union (IoU) of 97%, dice similarity coefficient (DSC) of 96% and 98% accuracy on testing data. The proposed architecture for pectoral muscle segmentation from the MLO views of mammogram images with high accuracy and dice score can be quickly merged with the breast tumor segmentation problem.

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Section: Related Workmentioning

confidence: 99%

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

Ali

Raza

Mahmood

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Most of the performance metrics encountered in the review include area under curve (AUC), sensitivity (Sn), specificity (Sp), accuracy (Acc), precision (P), recall (R), positive predictive values (PPV), Matthews correlation coefficient (MCC), geometric mean (G-Mean), which are usually successful in describing the classification performance [ 8 , 9 ]. Performance measures including Dice similarity coefficient (DSC) or Zijdenbos similarity index (ZSI) or F1-score, Hausdorff distance (H) and intersection over union (IoU) are the most effective metrics for measuring system’s segmentation performance [ 10 ]. Here, the true positives for the segmentation are the correctly labeled pixel while it is correctly labeled class for classification case.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning in Selected Cancers’ Image Analysis—A Survey

et al. 2020

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Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

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“…The total computational time of our method is shorter than most of that of other methods [64] [69] [70] [71], as shown in Table 20.…”

Section: Figure 32 Time Complexity Graph (Pectoral Muscle Removal Prmentioning

confidence: 96%

“…We believe that rather than focusing on curved boundaries in the processing stage itself, which increases the computation time, this can be tackled using the LUTs to extract the ROIs and regions within the ROIs. Rahimeto, S. et al, [64] in their research proposed an automatic pectoral muscle removal method based on the connected component labeling method. The muscle region was extracted using Otsu's multithresholding method.…”

Section: 06%mentioning

confidence: 99%

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

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Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image preprocessing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the "Rolling Ball Algorithm" and "Huang's Fuzzy Thresholding", which succeed in removing background from 100% of the images. For pectoral muscle removal "Canny Edge Detection" and "Hough's Line Transform" are used, which removed muscle from 99.06% of the images. "Invert", "CTI_RAS" and "ISOCONTOUR" lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

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Automatic pectoral muscle removal in mammograms

Cited by 28 publications

References 5 publications

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

Deep Learning in Selected Cancers’ Image Analysis—A Survey

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

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