Proceedings ArticleDOI
Automated breast profile segmentation for ROI detection using digital mammograms
Jawad Nagi,Sameem Abdul Kareem,Farrukh Nagi,Syed Khaleel Ahmed +3 more
- pp 87-92
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TLDR
An automated technique for mammogram segmentation that uses morphological preprocessing and seeded region growing (SRG) algorithm in order to remove digitization noises, suppress radiopaque artifacts, and remove the pectoral muscle, for accentuating the breast profile region is explored.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 imagesread more
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Journal ArticleDOI
Artificial intelligence in breast imaging.
TL;DR: Current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging are reviewed, with a gap in the market for contrast-enhanced spectral mammography AI-CAD tools.
A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer
TL;DR: The aim of this paper is to review existing approaches of preprocessing in mammographic images to improve the quality of the image and make it ready for further processing by removing the irrelevant noise and unwanted parts in the background of the mammogram.
Journal ArticleDOI
The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images
TL;DR: Various filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image are performed namely, average filter, adaptive median filter, average or mean filter, and wiener filter.
Journal ArticleDOI
Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms
TL;DR: The most often used methods for segmentation such as thresholding, morphology, region growing, active contours, and wavelet filtering are addressed and are the ones most used in the last decade by the majority of work published in this image processing domain.
Journal ArticleDOI
Pectoral muscle segmentation: A review
TL;DR: A conscious effort has been made to avoid deviating into the area of automated breast cancer detection by providing a comprehensive review of research papers in this area of pectoral muscle segmentation.
References
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Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Journal ArticleDOI
Seeded region growing
Richard Adams,Leanne Bischof +1 more
TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.
The Mammographic Image Analysis Society digital mammogram database
John Suckling,J. Parker,S. Astley,I. Hutt,C. Boggis,Ian W. Ricketts,E. Stamatakis,N. Cerneaz,SL Kok,P. Taylor,D. Betal,J. Savage +11 more
Journal ArticleDOI
Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images.
Fang-Fang Yin,Maryellen L. Giger,Kunio Doi,Charles E. Metz,Carl J. Vyborny,Robert A. Schmidt +5 more
TL;DR: A computerized scheme for the detection of masses in digital mammograms based on the deviation from the normal architectural symmetry of the right and left breasts, a bilateral subtraction technique is used to enhance the conspicuity of possible masses.