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الهدف من هذا البحث هو استعمال الشبكة العصبونية ذات الانتشار العكسي BNN في تصنيف كتل الثدي من صور الماموغرام بهدف تخفيض عدد الخزعات الجراحية غيـر الضـرورية. قارنا في هذه الدراسة أداء تصنيف كتل الثدي في صور الماموغرام بين الشبكة العصـبونية ذات الانتشار العكسي (BNN (Network Neural Backpropagation و بين أطبـاء أشـعة. دخل BNN هو الصفات الشكلية وصفات الكسوة المستخلصة من الكتل.
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References used
Giger ML, Computer Aided Diagnosis. In: Haus AG, Yaffe MJ, eds. Syllabus: a categorical course in physics – technical aspects of breast imaging. Oak Book, III: Radiological Society of North America,1993
Chan HP, et al, Improvement of radiologist’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology,1999
لينا عربش, الكشف المحوسب عن كتل الثدي من صور الماموغرام, . رسالة ماجستير, جامعة دمشق, 1998
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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 dependin
g 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.
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.
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%).
يشير حدوث التكلسات في الثدي إلى احتمال مرض سرطان الثدي عند المرأة. فـي صـورة
الماموغرام، تختلف التكلسات الخبيثة عادةً عن التكلسات السليمة من حيث الشكل و كيفية
التوزع. يهدف هذا البحث إلى تطوير طريقة من أجل التمييز الآلي بين التكلسات الخبيثـة و
التك
لسات السليمة في صور الثدي الشعاعية المرقمنة. انتخبـت عشـرة قياسـات مختلفـة
لتحقيق الهدف من هذا البحث. نُفّذت هذه القياسات على جميع التكلسات فـي ١٦ صـورة
جزئية من ١٦ ماموغراماً. اعتُمدت طريقة جديدة لتحليل النتـائج إِذْ يـتم حسـاب القيمـة
المتوسطة لأكبر ثلاث نتائج في كل قياس من القياسات المختلفة عوضاً عن اعتماد النتـائج
على كل تكلس بشكل منفرد. أظهرت النتائج أن ثلاثة مقاييس فقط يمكن استخدامها للتمييـز
الآلي بين التكلسات الخبيثة و التكلسات السليمة. هذه المقاييس هي مقياس محيط التكلس و
مقياس الدائرية و مقياس طول التكلس. أعطت الطريقة الجديدة نتائج يمكن الاعتماد عليهـا
من أجل التمييز الآلي بين آفات التكلسات الخبيثة و السليمة.
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.
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