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Register a new userAutomatic detection of breast lesions in mammograms images with features extraction using Otsu's method
الكشف الآلي عن آفات الثدي في صور الماموغرام مع استخلاص الخصائص باستخدام طريقة أوتسو Otsu
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غادة سعد( باحث )
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يعتبر الماموغرام الخيار الأفضل للكشف المبكر عن سرطان الثدي عند النساء، طورت أنظمة الكشف بمساعدة الحاسب (CAD) من أجل تحسين تشخيص صور الماموغرام. يقدم هذا البحث طريقة مقترحة لتجزيء صور الماموغرام آلياً اعتماداً على طريقة أوتسو Otsu’s method بهدف كشف آفة التكلسات الميكروية و الكتل من صور الماموغرام المجزأة ، تستند هذه التقنية المقترحة إلى ثلاث خطوات، أ ( تحديد مواصفات المنطقة ذات الاهتمام ROI, ب ( تحويل المويجي ثنائي البعد، ج ( تطبيق تعتيب أوتسو على ROI لاستخلاص الآفة. اختبرت الطريقة المقترحة على عدة صور أخذت من قاعدة بيانات معيارية mini-MIAS. و نفذت ضمن بيئة برنامج الماتلاب، . و يمكن إنجازها بشكل فعال على حاسب شخصي بسيط. بينت النتائج التجريبية و نتائج تقييم الأداء بأن الخوارزمية المقترحة تعتبر أداة مساعدة في تحسين أداء التشخيص، و لها القدرة على الكشف عن آفات الثدي.
A mammogram is the best option for early detection of breast cancer, Computer Aided Diagnostic systems(CADs) developed in order to improve the diagnosis of mammograms. This paper presents a proposed method to automatic images segmentation depending on the Otsu's method in order to detect microcalcifications and mass lesions in mammogram images. The proposed technique is based on three steps: (a) region of interest (ROI), (b) 2D wavelet transformation, and (c) OTSU thresholding application on ROI. The method tested on standard mini- MIAS database. It implemented within MATLAB software environment. Experimental results and performance evaluate results show that the proposed detection algorithm is a tool to help improve the diagnostic performance, and has the possibility and the ability to detect the breast lesions.
References used
SMITH R. A. 1993-Epidemiology of breast cancer in a categorical course in physics. Technical Aspects of Breast Imaging, 2nd ed. RSNA publication, Oak Book, II, pp.21
Peto.R, Boreham.J, Clarke.M, Davies.C, Beral.V, May 2000 -UK and USA Breast cancer deaths down 25% in year 2000 at ages 20-69 years. THE LANCET, Volume 355, Issue 9217 ،Page 1822, 20
Ghosh .R, Ghosh. M, Yearwood. J. April, 2004 -A Modular Framework for Multi category feature selection in Digital mammography. In Proceedings of the 12th European Symposium On Artificial Neural Networks ESANN’2004, Bruges (Belgium), pp. 175-180, 28-30
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Breast cancer is the second leading cause of death of women in the world. The early detection gives a
better chance to cure it. Physicians diagnose breast tumors by analyzing the characteristics of the lesion in
ultrasound images. Shape data, provi
ded by a tumor contour, is important to physicians in making
diagnostic decisions. However, due to the increasing use of technology in medicine, a computer aided
detection systems (CAD) have been built to help the expert. This research focuses on using a level-set
method as an effective lesion segmentation method for breast ultrasound images. By applying non-local
means filter on image, the unwanted speckle noise will be removed and the image's important details will
be preserved. Then the initial contours are sketched using the GUI in order to apply level-set method
which delineates the contour of the lesion in breast ultrasound image. The proposed method was found to
determine the breast tumor contours that are very similar to manual-sketched contours (about 96%).
This research aims to developing new method for breast tumors extraction and
features detection in breast magnetic resonance images by depending on clusteringand
image processing algorithms. At the beginning, one of clustering algorithms was used f
or
image segmentation and grouping pixels by their gray scale values. Then morphological
operations were implemented in order to remove noise and undesired regions, after that
suspected areas were extracted. Finally some shape features for extracted area were
detected, this features could be very useful for tumors diagnosis. A database consisted of
96breast magnetic resonance images were used and proposed approach was appliedby
MATLAB program, and we obtainedbreast tumors extraction and its features and
compared them with the doctor's opinion .
Breast cancer is the most widespread types of cancer among women. An efficient
diagnosis in its early stage can give women a better chance of full recovery. Calcification
is the important sign for early breast cancer detection. Mammography is the m
ost effective
method for breast cancer early detection using low radiation doses. The studies improved
the sensitivity of mammogram from 15% to 30% based on Computer Auto-Detection CAD
systems, which are used as a “second opinion” to alert the radiologist to structures that,
otherwise, might be overlooked. This article summarizes the various methods adopted for
micro-calcification cluster detection and compares their performance. Moreover, reasons
for the adoption of a common public image database as a test bench for CAD systems,
motivations for further CAD tool improvements, and the effectiveness of various CAD
systems in a clinical environment are given.
The Histogram of Oriented Gradient
(HOG) was used to construct the Support Vector Machine (SVM)
workbook. This method was applied using C++ programming
language and OpenCV and Dlib Libraries.
Mammography is widely used technique for breast cancer screening. There are
various other techniques for breast cancer screening but mammography is the most reliable
and effective technique. The images obtained through mammography are of low contra
st
which causes problem for the radiologists to interpret. Hence, a high quality image is
mandatory for the processing of the image for extracting any kind of information. Many
contrast enhancement algorithms have been developed over the years. This work presents a
method to enhancement Microcalcifications in digitized mammograms. The method is
based Mainly on the combination of Image Processing. The top-Hat and bottom–hat
transforms are a techniques based on Mathematical morphology operations. This
algorithm has been tested on mini-Mias database which have three types of breast tissues .
For evaluation of performance of image enhancement algorithm, the Contrast
Improvement Index (CII) and Peak Signal to Noise Ratio (PSNR) have been used.
Experimental results suggest that algorithm can be improve significantly overall
detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast.
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