An Overview of Pectoral Muscle Extraction Algorithms Applied to Digital Mammograms

Sapate, Suhas G.; Talbar, Sanjay N.

doi:10.1007/978-3-319-33793-7_2

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…Image texture examines information on spatial arrangements of color or intensity in a image. Using Gabor wavelets, dyadic wavelets, Hough transform and Radon transform, etc., the texture function and variation in intensity can be noted by decomposing the complicated picture into basic form [61]. The intensity level variation can be helpful for the radiologists to understand the mammography image more accurately.…”

Section: G Transform Based Methodsmentioning

confidence: 99%

“…Algorithm applied the logarithmic operation followed by Lloyed-Max algorithm. The energy lavel has been minimised by applying the greedy algorithm developed by Williams and Shah (1992) [61] although the active contour models reported excellent achievement in pectoral muscle removal and other breast areas But there are certain constraints, such as noise, fragile edges, amount of inner parameters, local minima lost at convergence time, lacking data about the range between the two pixels, which can be a barrier to define the pectoral muscle boundaries. Lei et al [72] propose the technique that is based on a discrete time Markov chain (DTMC) and an active contour model to detect the edge of the pectoral muscle.…”

Section: H Active Contour Methodsmentioning

confidence: 99%

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Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Saxena¹,

Singh²,

Agarwal³

2020

IJEAT

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Breast cancer is class of cancer that sets off when the breast cells grow out of proportion and control. The radiologist recognizes the sign of breast cancer by performing a kind of X-ray called screening mammography. During analysis of mammography the biggest problem arise because of the presence of pectoral muscle. The mass of tissue on which the breast is rest called the Pectoral muscle. The primary problem is that pectoral muscle area density is almost similar to the tumour cell and this condition generates confusion to recognize the tumour cell. For analysis the Medio-Lateral oblique (MLO) views of mammograms is being taken so that the complete breast image can be viewed. Some part of the pectoral muscle also gets visible along with the breast in the MLO view which must be segmented from the mammogram. Pectoral muscle involvement can lead to false positive or false negatives. The workforce shortages of Radiologist with respect to growing demands and to declare the result in a very short time have also increased the pressure. Consequently, a radiologist is sometimes unable to detect an anomaly. This is the time where CAD system can help radiologists to detect breast cancer at an earlier stage. Numerous strategies for the selection of the pectoral muscle have been suggested and developed so far. This article reviews the different segmentation techniques for pectoral muscle removal in mammograms through digital images..

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Section: G Transform Based Methodsmentioning

confidence: 99%

Section: H Active Contour Methodsmentioning

confidence: 99%

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Saxena¹,

Singh²,

Agarwal³

2020

IJEAT

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“…This step is important to achieve a more accurate classification in (CAD) diagnosis systems. Pectoral muscle removal is very complicated due to the following reasons In pectoral muscle, homogeneous area situated in the top left/right corner. The pectoral muscle contains brightest pixels in the mammogram. The pectoral muscle appears at approximately the same density as the dense tissue. The boundary of pectoral muscle is not a straight line, but can be convex, concave, or a mixture of both. Varying position, size, shape, and texture from image to image. Textural information in pectoral muscle is similar in most of cases to that of breast tissue. …”

Section: Methodsmentioning

confidence: 99%

Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients

Benhassine

Boukaache

Boudjehem

2019

Int J Imaging Syst Tech

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The purpose of this work is to develop a computer‐aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini‐Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.

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“…The pectoral mass is removed from the scanned image by global thresholding and BLOB analysis. Pectoral muscle [1] may increase the computational complexity of the detection process and also causes the reduction in detection accuracy. Hence to remove all these unnecessary parts from the breast region in the mammogram is a vital preprocessing task in CAD system of the breast cancer.…”

Section: Pre-processing and Image Enhancementmentioning

confidence: 99%

Improvement in Detection Accuracy of Digital Mammogram Using Point Transform and Data Mining Technique

Meharunnisa¹,

Ravishankar²,

Kalyanasundaram³

2018

IJIVP

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Cancer is one of the dangerous diseases faced by humans. Every one out of 100 women is facing breast cancer. So, to overcome this huge ratio many researches are being carried out. Prevention is better than cure; this paper presents one such attempt of detecting breast cancer in the early stages. In proposed method exponential point transform is carried out for image enhancement and in preprocessing stage pectoral mass is removed from the mammogram image. As the next step we apply K-means algorithm and morphological processing to identify the infected region and removal of unwanted region. Finally, Decision Tree Data mining technique is used for classifying features to detect presence of tumor. Hence by this approach we get more accurate results. The experimental results gave an accuracy of 97.03 %.

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An Overview of Pectoral Muscle Extraction Algorithms Applied to Digital Mammograms

Cited by 12 publications

References 35 publications

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients

Improvement in Detection Accuracy of Digital Mammogram Using Point Transform and Data Mining Technique

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