A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

Moghbel, Mehrdad; Ooi, Chia Yee; Ismail, Nordinah; Hau, Yuan Wen; Memari, Nogol

doi:10.1007/s10462-019-09721-8

Cited by 49 publications

(24 citation statements)

References 91 publications

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“…Different reviews about pectoral region segmentation were also presented. Moghbel et al 23 and Karthikeyan et al 24 presented a detailed review of the pectoral muscle segmentation methods.…”

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

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“…To present a more meaningful evaluation of the results of the second experiment some statistical performance measuring methods were used. The brief descriptions of these methods and of their variables are given as follows [5];…”

Section: Figurementioning

confidence: 99%

“…A general CAD system consists of preprocessing, localization and/or segmentation, feature extraction, and classification steps. Exceptionally, in deep learning-based methods, feature extraction, and classification can be combined in a single step [5]. CAD systems can be analyzed in two groups in terms of the evaluated images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Toz

Erdoğmuş

2021

IEEE Access

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Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.

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“…However, invasive carcinoma is a malignant tumor that tends to spread in other organs. There are many techniques for breast cancer detection, such as X-ray mammography [4], 3-D Ultrasound (US) [5], Computed Tomography (CT), Positron Emission Tomography (PET) [6], Magnetic Resonance Imaging (MRI) [7], and breast temperature measurement [8]. However, pathological diagnosis is often regarded as a ''golden standard'' [9].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

Cited by 49 publications

References 91 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

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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