Automatic detection of breast border and nipple in digital mammograms

Méndez, Adriana; Tahoces, Pablo G.; Lado, Marı́a J.; Souto, Miguel Abel; Correa, J. L. del Río; Vidal, José Martı́nez

doi:10.1016/0169-2607(96)01724-5

Cited by 109 publications

(46 citation statements)

References 14 publications

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“…Few mammogram segmentation algorithms have been tested extensively. Abdel-Mottaleb et al [32] test 500 mammograms, with their algorithm finding an "acceptable" boundary in 98% of the images. Méndez et al [33] test their algorithm on 156 mammograms of which the breast contour is deemed to be "accurate" or "nearly accurate" in 89% of the images.…”

Section: Discussionmentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

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Section: Discussionmentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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“…AbdelMottaleb et al [22] use a system of masking images with different thresholds to find the breast edge. Méndez et al [18] found the breast contour using a gradient based method. They first use a two-level thresholding technique to isolate the breast region of the mammogram.…”

Section: Literature Surveymentioning

confidence: 99%

“…There have been various approaches proposed to the task of segmenting the breast profile region in mammograms. Some of these have focused on using thresholding [16] [17], gradients [18], modelling of the non-breast region of a mammogram using a polynomial [19], or active contours [7].…”

Section: Literature Surveymentioning

confidence: 99%

Automated breast profile segmentation for ROI detection using digital mammograms

Nagi

Kareem

Nagi

et al. 2010

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

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Abstract-Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region. To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics. Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images.

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“…Méndez et al [32] developed a fully automatic technique to detect the border of the breast and the nipple. An algorithm that computes the gradient of gray levels is used to detect the breast border.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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Automatic detection of breast border and nipple in digital mammograms

Cited by 109 publications

References 14 publications

Review on Mammogram Mass Detection by Machine Learning Techniques

Review on Mammogram Mass Detection by Machine Learning Techniques

Automated breast profile segmentation for ROI detection using digital mammograms

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

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