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Article
Application of GLCM technique on Mammograms for Early Detection of Breast Cancer

Author: Jinan FadhilMahdi
Journal: Journal of University of Babylon مجلة جامعة بابل ISSN: 19920652 23128135 Year: 2015 Volume: 23 Issue: 2 Pages: 885-890
Publisher: Babylon University جامعة بابل

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Abstract

Breast cancer represents the second leading cause of cancer deaths in women today، and it is the most common type of cancer in women. It has become major health issue in the world. Mammography is the main test used for screening and early diagnosis. Early detection performed on X-ray mammography is the key to improve breast cancer prognosis. GLCM technique introduced by Haralick is mostly used in image processing to study the grey levels intensities present in the image, Haralick features also known as first order have been used for detection of malignant masses in breast tissue images, I have attempted firstly to obtain well known Haralick features as well as the second order GLCM features and have studied its effects in breast cancer image classification.

باستخدام التفاصيل اللازمة نتنبئ بالأسباب الممكنة لحدوث سرطان الثدي فبالرغم من تقدم التقنيات للكشف عن سرطان الثدي مبكراً يبقى الماموكرام هو الاساس وخاصتاً في الدول طور التقدم حيث تكاليفه رخيصة وغير اقتحاميه تجعلها تستعمل بشكل واسع فعال تقنيناً .ودراستنا الحالية والدراسات التمهيدية المشار لها تجعل النتائج مؤثره وفعاله من عدة عوامل وتشمل نقاوة بكسل وكذالك خطوات حسابيه والاتجاه والمسافة في هذا النوع من GI.CMS


Article
Contrast Enhancement in Gray Level Images

Authors: Duha Amir Sultan --- Alhan Anwar Yonis
Journal: JOURNAL OF EDUCATION AND SCIENCE مجلة التربية والعلم ISSN: 1812125X Year: 2019 Volume: 28 Issue: 2 Pages: 259-281
Publisher: Mosul University جامعة الموصل

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Abstract

Contrast enhancement is a very important step in image processing, pattern recognition and computer vision .Histogram Equalization (HE) is widely used for contrast Enhancement . This paper discussed five techniques of contrast enhancement, i.e. Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness preserving Bi_Histogram Equalization (BBHE), Dualistic Sub_Image Histogram Equalization (DSIHE) and Recursive Sub_Image Histogram equalization (RSIHE). Also it presented the comparison among the various techniques that show the image enhances the overall contrast and visibility of local details, by using two measures: Peak-Signal-to-Noise-Ratio (PSNR) and Absolute Mean Brightness Error (AMBE) to check the signal and brightness power respectively. This work is programmed by MATLAB VER.7.8.

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