Pectoral Muscle Region Research Articles

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency

PurposeAccurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra).MethodsA pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine.ResultsOur proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra’s estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram).ConclusionWe present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Segmentation of Pectoral Muscle in Mammograms Using Granular Computing

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Due to the poor contrast, noise and artifacts which results in difficulty for radiologists to diagnose, Computer-Aided Diagnosis (CAD) systems are hence developed. The extraction of breast region is a fundamental and crucial preparation step for further development of CAD systems.OBJECTIVE: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed.METHODS: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using CLAHE and semantic segmentation using Deeplab v3 model.RESULTS: The method is trained and evaluated on mini-MIAS dataset. It has also been evaluated on INbreast dataset. The results outperform those generated by other recent researches and are indicative of the capacity of the model to retain its accuracy and runtime advantage across different databases with different image resolutions.CONCLUSIONS: The proposed method shows state-of-the-art performance at extracting breast region from mammographic images. Wide range of evaluation on two commonly used mammography datasets proves the ability and adaptability of the method.

Automatic segmentation of the pectoral muscle based on boundary identification and shape prediction

The purpose of this work is to identify the pectoral muscle region in mediolateral oblique (MLO) view mammograms even when the boundary is blurred or obscured. The problem is decoupled into two subproblems in our study: identifying parts of boundaries with high confidence and predicting the overall shape of the pectoral muscle. Due to the similarity in intensity and texture between pectoral muscle and gland tissue, we trained a deep neural network to distinguish them in the first subproblem. The boundary with high confidence can be obtained according to the consistency of predictions from multiple converged models. For the shape prediction problem, a generative adversarial network (GAN) is used to learn mapping from a given identified region and the breast shape to the overall pectoral muscle shape. Our method is evaluated on a mammogram dataset including 633 MLO view mammograms collected from three different datacenters. We take U-Net as our baseline model and the dataset is divided into three groups according to the performance of U-Net for evaluation. In all three groups, U-Net achieves 80.1%, 92.9%, and 98.3% in the Dice similarity coefficient, respectively, and our method achieves 85.2%, 94.8%, and 98.1% in the Dice similarity coefficient, respectively. The experiment shows that our method effectively estimates the pectoral muscle boundary, even parts of boundaries that are difficult to detect, and greatly improves the performance of segmentation in this case.

Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours

Automatic detection of the pectoral muscle in mammograms is widely used in computer-aided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study, we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.

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

Mammography can be considered as the current gold standard for detecting early signs of breast cancer and is in wide use throughout the world. As confirmed by many studies, breast cancer screening using mammography can reduce breast cancer-related mortality by 30–70%. However, although the interpretation of mammography images by a second reader has been shown to increase the cancer detection rate, this practice is not widespread due to the cost associated. As a result, computer-aided detection/diagnosis (CAD) of breast mammography has been gaining popularity with various studies illustrating the positive effects of using computers in detecting early breast cancer signs by providing the radiologists with a second opinion with most of these CAD systems requiring the breast outline and pectoral muscle regions (in images acquired using Medio-Lateral-Oblique view) to be segmented from mammograms prior to the classification. This paper discusses recent developments and methods proposed for segmenting the breast and pectoral muscle regions and compares the performance and shortcomings of different approaches grouped together based on the techniques used. While it is arduous to compare these methods using comparative analysis, a set of common performance evaluation criterion is defined in this study and various methods are compared based on their methodology and the validation dataset used. Although many methods can achieve promising results, there is still room for further development, especially in pre-processing and image enhancement steps where most methods do not take the necessary steps for ensuring a smooth segmentation of boundaries. In this paper, the most effective pre-processing, image enhancement and segmentation concepts proposed for breast boundary and pectoral muscle segmentation are identified and discussed in hopes of aiding the readers with identifying the best possible solutions for these segmentation problems.

Removal of pectoral muscle based on topographic map and shape-shifting silhouette

BackgroundIn digital mammography, finding accurate breast profile segmentation of women’s mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing.MethodsWe propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images.ResultsTo assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth.ConclusionsOur approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.

A quality enhanced preprocessing method for mammogram ROI extraction

Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.

Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.

In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.

A novel multistage system for the detection and removal of pectoral muscles in mammograms

In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

Detection of architectural distortion in mammograms using geometrical properties of thinned edge structures

AbstractThe proposed method detects the most commonly missed breast cancer symptom, Architectural Distortion. The basis of the method lies in the analysis of geometrical properties of abnormal patterns that correspond to Architectural Distortion in mammograms. Pre-processing methods are employed for the elimination of Pectoral Muscle (PM) region from the mammogram and to localize possible centers of Architectural Distortion. Regions that are candidates to contain centroids of Architectural Distortion are identified using a modification of the isotropic SUSAN filter. Edge features are computed in these regions using Phase Congruency, which are thinned using Gradient Magnitude Maximization. From these thinned edges, relevant edge structures are retained based on three geometric properties namely eccentricity to retain near linear structures, perpendicular distance from each such structure to the centroid of the edges and quadrant support membership of these edge structures. Features for classification are g...

IPES: An Image Processing-Enabled Expert System for the Detection of Breast Malignant Tumors

Mammography is an influential screening tool for preliminary detection of breast carcinoma. However, Interpreting mammograms is exhausting, particularly in the screening context. Moreover, sensitivity of mammography based screening is influenced by image quality and the experience level of radiologist. Thus, computer-aided diagnosis (CAD) programs can be utilized as a second-opinion tools that enhance the performance of radiologists, by consolidating sensitivity rates in contrast with those taken by double readings. The current paper is directed towards the integration of image processing and the rule-based reasoning into a diagnostic expert system for breast tumors. A proposed system termed IPES (Image Processing-enabled Expert System) is developed for the detection of breast malignant tumors, appear in mammography in three steps: (1) segmentation of mammographic masses from both pectoral muscle region and breast tissues, (2) Characterization of segmented masses upon the standards of Breast Imaging Reporting and Data System (BI-RADS) by: (i) shape-relevant features; (ii) margin characteristics; and (iii) density features, and (3) diagnosis of mass type upon some inference rules. The data set used for testing IPES contained 540 samples obtained from the standard Digital Database for Screening Mammography (DDSM). The Receiver Operator Characteristic (ROC) curves have been employed to evaluate the sensitivities and specificities of the system. Finally, the results reveal the efficacy of IBES in discriminating both malignant and benign breast masses.

Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms.

The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.

Pectoral Muscle Segmentation in Mammograms Based on Anatomic Features

In this paper, an anatomic features-based method is proposed to segment the pectoral muscle region according to texture feature and shape feature. Firstly, spectral clustering combined with edge information is presented and used to segment the pectoral muscle, leading to an over-segmented result. Afterwards, a region merging algorithm is proposed on the basis of the intensity distribution of regions and the characteristic of triangle shape. Finally, the pectoral muscle is identified from the over-segmented result according to region merging. The proposed method is evaluated on 322 mammograms from the mammographic image analysis society (mini-MIAS) database and 50 mammograms from the Breast Center of Peking University People s Hospital (BCPKUPH). The results show that the proposed method works well for pectoral muscles of different sizes, shapes and intensities.

Breast cancer risk analysis based on a novel segmentation framework for digital mammograms.

The radiographic appearance of breast tissue has been established as a strong risk factor for breast cancer. Here we present a complete machine learning framework for automatic estimation of mammographic density (MD) and robust feature extraction for breast cancer risk analysis. Our framework is able to simultaneously classify the breast region, fatty tissue, pectoral muscle, glandular tissue and nipple region. Integral to our method is the extraction of measures of breast density (as the fraction of the breast area occupied by glandular tissue) and mammographic pattern. A novel aspect of the segmentation framework is that a probability map associated with the label mask is provided, which indicates the level of confidence of each pixel being classified as the current label. The Pearson correlation coefficient between the estimated MD value and the ground truth is 0.8012 (p-value < 0.0001). We demonstrate the capability of our methods to discriminate between women with and without cancer by analyzing the contralateral mammograms of 50 women with unilateral breast cancer, and 50 controls. Using MD we obtained an area under the ROC curve (AUC) of 0.61; however our texture-based measure of mammographic pattern significantly outperforms the MD discrimination with an AUC of 0.70.

Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION

A simple edge-based preprocessing scheme is proposed in this paper for contrast enhancement of digital mammogram images while preserving the edges more accurately. This proposed method has three steps: (i) initially the breast region is segmented from the mammogram images by removing the film artifacts, (ii) the pectoral muscle region is identified and excluded from the breast region using a novel adaptive thresholding method, and (iii) an Improved Watershed Segmentation (IWS) is applied to segment the breast profile, and each region is enhanced with simple histogram equalization. The segmentation is performed in order to achieve adaptive contrast enhancement. The performance of this proposed pectoral removal method is analyzed with two measures: Hausdorff Distance (HD) and Mean of Absolute Error Distance (MAED), and the proposed contrast enhancement approach is been analyzed with the five diverse parameters along with the classification accuracy. The experiments and results show the potential performance of our proposed algorithm over the existing approaches with optimum results on all the performance measure and the classification performance is been evaluated with a hybrid neural network, our proposed method proves the better performance with the achievement of 92% accuracy.

OCCURRENCE OF THE SUBCUTANEOUS MITE,HYPODECTES PROPUS(ACARINA: HYPODERATIDAE), IN THE GREY HERON,ARDEA CINEREA(AVES: CICONIIFORMES) A NEW HOST RECORD IN JAPAN

An adult male grey heron, Ardea cinerea (Aves: Ciconiiformes), was rescued in Mikunigaoka 590-0021, Sakai, Osaka, Japan, and euthanized because of severe injury to both legs. At necropsy, a large number of deutonymphs (hypopi) of the hypoderatid mite, Hypodectes propus (Acarina: Hypoderatidae), were found in the subcutis and in the fasciae of the adipose tissue in the pectoral muscle and abdominal regions. The mites were 1.26 mm in length and 0.35 mm in width on average. The present hypopi were identified as H. propus, based on the dimensions of the mite, together with the distinct typical coxal apodemes in the anterior part. The present case reported the subcutaneous mite, H. propus, in the grey heron, A. cinerea, as a new host record in Japan.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c‐means clustering and support vector machine segmentation

The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Removal of Pectoral Muscle Region in Digital Mammograms using Binary Thresholding

The pectoral muscle represents a predominant density region in Medio-Lateral Oblique (MLO) views of mammograms, which appears at approximately the same density as the dense tissues of interest in the image and can affect the results of image analysis methods. Therefore, segmentation of pectoral muscle is important in order to limit the search for the breast abnormalities only to the breast region. In this paper, a simple and effective approach is proposed to exclude the pectoral muscle based on binary operation. The performance is analyzed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on differences between the results received from the radiologists and by the proposed method. The digital mammogram images are taken from MIAS dataset which contains 322 images in total, out of which the proposed algorithm able to detect and remove the pectoral region from 291 images successfully.