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Open AccessJournal ArticleDOI

Contrast Enhancement of Mammograms for Rapid Detection of Microcalcification Clusters

TLDR
The proposed enhancement method could be helpful for radiologists to easily detect MCCs; it could also decrease the number of biopsies and reduce the frequency of clinical misdiagnosis and it could be useful prior to segmentation or classification stages.
Abstract
Introduction Breast cancer is one of the most common types of cancer among women. Early detection of breast cancer is the key to reducing the associated mortality rate. The presence of microcalcifications clusters (MCCs) is one of the earliest signs of breast cancer. Due to poor imaging contrast of mammograms and noise contamination, radiologists may overlook some diagnostic signs, specially the presence of MCCs. In order to improve cancer detection, image enhancement methods are often used to aid radiologists. In this paper, a new enhancement method was presented for the accurate and early detection of MCCs in mammograms. Materials and Methods The proposed system consisted of four main steps including: 1) image scaling;2) breast region segmentation;3) noise cancellation using a filter, which is sensitive to MCCs; and 4) contrast enhancement of mammograms using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and wavelet transform. To evaluate this method, 120 clinical mammograms were used. Results To evaluate the performance of the image enhancement algorithm, contrast improvement index (CII) was used. The proposed enhancement method in this research achieved the highest CII in comparison with other methods applied in this study. The Validity of the results was confirmed by an expert radiologist through visual inspection. Conclusion Detection of MCCs significantly improved in contrast-enhanced mammograms. The proposed method could be helpful for radiologists to easily detect MCCs; it could also decrease the number of biopsies and reduce the frequency of clinical misdiagnosis. Moreover, it could be useful prior to segmentation or classification stages.

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Citations
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Journal ArticleDOI

An enhanced mammogram diagnosis using shift-invariant transform

TL;DR: The proposed NonSubsampled Contourlet Transform method extracts the shift-invariant multi-scale, multi-direction and the geometric information of mammogram images which is used to distinguish noise from weak edges than existing transformations.
Proceedings ArticleDOI

Evaluation of digital filters for similarity analysis between tomosynthesis and 2d mammographic images

TL;DR: In this paper, the similarity between 2D and 3D tomosynthesis images using structural similarity index (SSIM) was quantified using a region of interest of a 2D conventional mammography acquired in combo mode.
Proceedings ArticleDOI

New breast cancer detection algorithm

TL;DR: A new algorithm based in Crεmε Filter, which permitting to observe texture when parameter is changed, is presented for breast cancer detection, showing micro calcifications with better contrast, allowing a better detection of cancer.
Book ChapterDOI

Split and Merge Multi-scale Retinex Enhancement of Magnetic Resonance Medical Images

TL;DR: This paper presents the Multi-Scale Retinex (MSR) enhancement of Magnetic Resonance (MR) medical images using split and merge technique and experimental results presented confirms that the proposed method outperforms compared to existing methods.
Proceedings ArticleDOI

Evaluation of Brest Cancer Detection Algorithm

TL;DR: The evaluation of an algorithm for detection of microcalcifications in breast images, allowing us early breast cancer prevention and the Crème Filter, an algorithm with only one parameter “n” that permits to observe the texture dependence when the parameter is changed.
References
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Journal ArticleDOI

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
Journal ArticleDOI

Computer-aided detection and classification of microcalcifications in mammograms: a survey

TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.
Journal ArticleDOI

A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films

TL;DR: A computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms gives quite satisfactory detection performance.
Journal ArticleDOI

Screening mammograms: interpretation with computer-aided detection--prospective evaluation.

TL;DR: The use of CAD improved the detection of breast cancer, with an acceptable increase in the recall rate and a minimal increase inThe number of biopsies with benign results.
Journal ArticleDOI

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

TL;DR: A novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed, which seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.
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