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Using Top-Hat and Bottom-Hat Transforms for Contrast Enhancement of Mammograms

استخدام تحويلي TOP-HAT و BOTTOM-HAT لتحسين تباين صور الماموغرام

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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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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 contrast 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.


Artificial intelligence review:
Research summary
يهدف هذا البحث إلى تحسين تباين صور الماموغرام باستخدام تحويلي Top-Hat و Bottom-Hat اللذين يعتمدان على العمليات المورفولوجية الرياضية. تعتبر صور الماموغرام أداة فعالة للكشف المبكر عن سرطان الثدي، لكن التباين المنخفض لهذه الصور يشكل تحدياً لأطباء الأشعة. تم اختبار الطريقة المقترحة على مجموعة من الصور من قاعدة بيانات mini-Mias، وتم تقييم الأداء باستخدام معيار تحسين التباين (CII) ونسبة ذروة الإشارة إلى الضجيج (PSNR). أظهرت النتائج أن الخوارزمية المقترحة قادرة على تحسين نظام التشخيص بمساعدة الحاسب (CAD) خاصة للأنسجة الكثيفة. تعتمد الطريقة على إزالة الضجيج باستخدام مرشح Wiener، ثم تطبيق تحويلي Top-Hat و Bottom-Hat، وأخيراً تحسين التباين المحلي بإضافة الصورة الأصلية إلى الفرق بين الصورتين الناتجتين من التحويلين. تم تقييم الأداء من خلال معايير كمية مثل CII و PSNR، وأظهرت النتائج تحسناً ملحوظاً في تباين الصور وخاصة للأنسجة الكثيفة.
Critical review
تقدم الدراسة خوارزمية فعالة لتحسين تباين صور الماموغرام باستخدام العمليات المورفولوجية، وقد أظهرت النتائج تحسناً ملحوظاً في تباين الصور وخاصة للأنسجة الكثيفة. ومع ذلك، يمكن أن تكون هناك بعض النقاط التي تحتاج إلى مزيد من البحث والتطوير. على سبيل المثال، قد يكون من المفيد مقارنة الخوارزمية المقترحة مع تقنيات أخرى حديثة لتحسين التباين. كما أن الدراسة لم تتناول بشكل كافٍ تأثير الضوضاء على نتائج التحسين، وهو ما يمكن أن يكون له تأثير كبير على جودة الصور النهائية. بالإضافة إلى ذلك، قد يكون من المفيد تطبيق الخوارزمية على مجموعة أكبر من الصور للتحقق من عموميتها وفعاليتها في مختلف الحالات.
Questions related to the research
  1. ما هي التقنية الأساسية المستخدمة في هذا البحث لتحسين تباين صور الماموغرام؟

    التقنية الأساسية المستخدمة هي تحويلي Top-Hat و Bottom-Hat اللذين يعتمدان على العمليات المورفولوجية الرياضية.

  2. ما هي قاعدة البيانات التي تم استخدامها لاختبار الخوارزمية المقترحة؟

    تم استخدام قاعدة بيانات mini-Mias لاختبار الخوارزمية المقترحة.

  3. ما هي المعايير التي تم استخدامها لتقييم أداء الخوارزمية؟

    تم استخدام معيار تحسين التباين (CII) ونسبة ذروة الإشارة إلى الضجيج (PSNR) لتقييم أداء الخوارزمية.

  4. ما هي الخطوة الأولى في منهجية البحث لتحسين تباين صور الماموغرام؟

    الخطوة الأولى هي إزالة الضجيج من الصورة باستخدام مرشح Wiener.


References used
BORING,C.C., SQUIRES, T.S. and TONG, T.; "Cancer statistics, 1992, CA", A Cancer Journal for Clinicians, Vol. 42, No. 1, pp. 19-38, 1992
JOHNS .P.C., YAFFE .M.J.,“X-ray characterization of normal and neoplastic breast tissues,” Physics Medical and Biology, Vol.32, no. 6, 1987, 675-695
THANGAVEL .K., KARAN .M., SIVAKUMAR .R., KAJA MOHIDEEN.A., “Automatic detection of microcalcification in mammograms: a review,” ICGST-GVIP Journal, Volume (5), Issue (5), May 2005
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