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Register a new userBreast tumors extraction and features detection in breast magnetic resonance images using clustering and image processing algorithms
استخراج الأورام السرطانية و تحديد واصفاتها في صور المرنان المغناطيسي للثدي باستخدام خوارزميات العنقدة و معالجة الصورة
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بلسم عيد( باحث )
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يهدف البحث إلى تطوير طريقة جديدة لاستخراج و تحديد خصائص و سمات الأورام السرطانية في صور المرنان المغناطيسي للثدي بالإعتماد خوارزميات العنقدة و معالجة الصور الرقمية ,تم في البداية الاعتماد على إحدى خوارزميات العنقدة فيتجزئة الصورة و تجميع عناصرها و فققيم السويات الرمادية و من ثم تم تطبيق العمليات المورفولوجية و ذلك للتخلص من الضجيج و حذف المعلومات غير المرغوبة و بالتالي استخراج المنطقة المشبوهة و أخيراً تم استخلاص بعض الواصفات الشكلية و التي يمكن ان تكون مفيدة في تشخيص المنطقة المشبوهة المستخرجة ,استخدمت قاعدة بيانات مكونة من 96 صورة من صور المرنان المغناطيسي للثدي و تم تطبيق الطريقة المقترحة عليها باستخدام برنامج الماتلاب حيث تم استخراج المناطق الورمية من هذه الصور و مقارنتها مع رأي الأطباء.
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 for 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 .
References used
B.Senthilkumar,G.Umamaheswari,Combination of Novel Enhancement Technique and Fuzzy C Means Clustering Technique in Breast Cancer Detection. Biomed Res-India 2013 Volume 24 Issue 2,252-257
S.SAHEB BASHA, DR.K.SATYA PRASAD, Automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c –means clustering. Journal of Theoretical and Applied Information Technology. 2009,704-709
VALLIAPPAN Raman, PUTRA Sumari, MANDAVA Rajeswari, A Theoretical Methodology and Prototype Implementation for Detection Segmentation Classification of Digital Mammogram Tumor by Machine Learning. IJCSI International Journal of Computer Science Issues. Vol. 7, Issue 5, September 2010,38-44
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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.
112 patients with suspected breast lesions were investigated using Scintimammography with 99mTc-MIBI, mammography and magnetic resonance to compare the diagnostic accuracy of Scintimammography with that of mammography and magnetic resonance in the d
etecting of primary breast cancer. Excisional biopsy was taken after 7-10 days of Scintimammography. 70 patients were diagnosed with breast cancer; 55 palpable and 15 non-palpable lesions, while 42, 30 palpable and 12 non-palpable were diagnosed as benign lesions. The sensitivity of the three methods were high and approximate (89% by Scintimammography, 90% by mammography and 94% by magnetic resonance), while Scintimammography showed higher specificity (86%) versus 21% for mammography and 50% for magnetic resonance. The high specificity in addition to high positive (91%) and negative (82%) predictive values of Scintimammography resulted in a highly accurate diagnostic (87%) method compared to mammography (64%) and magnetic resonance (78%) procedures. Through the complementary use of Scintimammography it is possible to increase the sensitivity for the detection of breast cancer. In patients in whom the status of a palpable breast mass remains unclear, Scintimammography may help to reduce the amount of unnecessary biopsies.
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%).
choose the right way to dividing set of data with high dimensions to clusters in specific field and comparison the different subspace clustering algorithms and present the applications and usage
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.
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