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Title: Digital Processing and Segmentation of Breast Microcalcifications Images Obtained by a Si Microstrips Detector: Preliminary Results

Abstract

We present the preliminary results of digital processing and segmentation of breast microcalcifications images. They were obtained using a Bede X ray tube with Cu anode, which was fixed at 20 kV and 1 mA. Different biopsies were scanned using a 128 Si microstrips detector. Total scanning resulted in a data matrix, which corresponded with the image of each biopsy. We manipulated the contrast of the images using histograms and filters in the frequency domain in Matlab. Then we intended to investigate about different contour models for the segmentation of microcalcifications boundaries, which were based on the contrast and shape of the image. These algorithms could be applied to mammographic images, which may be obtained by digital mammography or digitizing conventional mammograms.

Authors:
 [1];  [2]
  1. Departamento de Fisica, Centro de Investigacion y de Estudios Avanzados del IPN, A. P. 14-740, 07000 Mexico, D. F. (Mexico)
  2. C.U.C.E.I. Universidad de Guadalajara, Av. Revolucion 1500, 1100, Guadalajara, Jal. (Mexico)
Publication Date:
OSTI Identifier:
21054784
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 885; Journal Issue: 1; Conference: EAV06: Advanced summer school in physics 2006: Frontiers in contemporary physics, Mexico City (Mexico), 10-14 Jul 2006; Other Information: DOI: 10.1063/1.2563194; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; ANODES; BIOMEDICAL RADIOGRAPHY; BIOPSY; FILTERS; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; NEOPLASMS; SHAPE; SI MICROSTRIP DETECTORS; X-RAY TUBES

Citation Formats

Diaz, Claudia C., and Angulo, Abril A. Digital Processing and Segmentation of Breast Microcalcifications Images Obtained by a Si Microstrips Detector: Preliminary Results. United States: N. p., 2007. Web. doi:10.1063/1.2563194.
Diaz, Claudia C., & Angulo, Abril A. Digital Processing and Segmentation of Breast Microcalcifications Images Obtained by a Si Microstrips Detector: Preliminary Results. United States. doi:10.1063/1.2563194.
Diaz, Claudia C., and Angulo, Abril A. Fri . "Digital Processing and Segmentation of Breast Microcalcifications Images Obtained by a Si Microstrips Detector: Preliminary Results". United States. doi:10.1063/1.2563194.
@article{osti_21054784,
title = {Digital Processing and Segmentation of Breast Microcalcifications Images Obtained by a Si Microstrips Detector: Preliminary Results},
author = {Diaz, Claudia C. and Angulo, Abril A.},
abstractNote = {We present the preliminary results of digital processing and segmentation of breast microcalcifications images. They were obtained using a Bede X ray tube with Cu anode, which was fixed at 20 kV and 1 mA. Different biopsies were scanned using a 128 Si microstrips detector. Total scanning resulted in a data matrix, which corresponded with the image of each biopsy. We manipulated the contrast of the images using histograms and filters in the frequency domain in Matlab. Then we intended to investigate about different contour models for the segmentation of microcalcifications boundaries, which were based on the contrast and shape of the image. These algorithms could be applied to mammographic images, which may be obtained by digital mammography or digitizing conventional mammograms.},
doi = {10.1063/1.2563194},
journal = {AIP Conference Proceedings},
number = 1,
volume = 885,
place = {United States},
year = {Fri Feb 09 00:00:00 EST 2007},
month = {Fri Feb 09 00:00:00 EST 2007}
}
  • We studied the capability of Matlab in digital processing of breast tissues images with microcalcifications. We obtained digital images of different byopsies through a Bede X-ray tube, fixed at 20 kV and 1 mA. Radiation exposition time was varied. The byopsies were placed between a 120{mu}m collimator and a 128 strips detector, which was used to measure the absorption of X rays in the tissue. Matlab allowed the manipulation of digital images, and this software was intended to improve the identification of microcalcifications in breast tissues.
  • Double-sided microstrip silicon crystals are being tested as detectors for X-rays in the diagnostic energy range (10-100 kcV) for digital radiography. The authors have developed an ADC and CAMAC-based acquisition system to study the imaging capabilities of a silicon [mu]strip detector with 100 and 200 [mu]m read-out pitch. They present the first images of submillimeter high contract phantoms obtained with an X-ray mammography tube operating at high flux density.
  • Purpose: 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') andmore » 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. Methods: 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. Results: 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 {kappa}{>=} 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). Conclusions: 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.« less
  • I present some results of contrast enhancement and segmentation of microcalcifications in digital mammograms. These mammograms were obtained from MIAS-minidatabase and using a CR to digitize images. White-top-hat and black-top-hat transformations were used to improve the contrast of images, while reconstruction-by-dilation algorithm was used to emphasize the microcalcifications over the tissues. Segmentation was done using different gradient matrices. These algorithms intended to show some details which were not evident in original images.
  • The present paper synthesizes the results obtained in the evaluation of a 64 microstrips crystalline silicon detector coupled to RX64 ASIC, designed for high-energy physics experiments, as a useful X-ray detector in advanced medical radiography, specifically in digital mammography. Research includes the acquisition of two-dimensional radiography of a mammography phantom using the scanning method, and the comparison of experimental profile with mathematically simulated one. The paper also shows the experimental images of three biological samples taken from breast biopsies, where it is possible to identify the presence of possible pathological tissues.