روش جديد آسيب‌شناسي توده‌ها در تصاوير ماموگرافي به كمک ترکیب ويژگي‌هاي منطبق بر استاندارد BI - RADS و كلاسه‌بندي كننده مبتني بر تضاد
الموضوعات :
فاطمه ساکی
1
(دانشگاه علم و صنعت ايران)
امیر طهماسبی
2
(دانشگاه علم و صنعت ايران)
شهریار برادران شکوهی
3
(مهندسی برق)
الکلمات المفتاحية: استخراج ويژگي استاندارد BI-RADS سيستم CADx کلاسه‌بندی کننده مبتنی بر تضاد ماموگرافي,
ملخص المقالة :
تفكيك توده‌هاي خوش‌خيم و بدخيم در ماموگرام‌هاي ديجيتالي يكي از مراحل بسيار مهم تشخيص زودهنگام سرطان سينه است، چرا كه مي‌تواند تا حد زيادي شانس بقاي بيمار را افزايش دهد. در اين مقاله يك سيستم CADx نوين با به‌کارگيري کلاسه‌بندي کننده جديد مبتني بر تضاد (OWBP) جهت آسيب‌شناسي توده‌ها در تصاوير ماموگرافي معرفي خواهد شد. هدف، بهبود عملکرد و سرعت يادگيري الگوريتم‌هاي CADx با استفاده از ترکيب ويژگي‌هاي منطبق بر استاندارد BI-RADS و كلاسه‌بندي كننده پيشنهادي مي‌باشد. ورودي سيستم يک ROI بوده که حاوي يک توده مشکوک است. اين ناحيه ابتدا تحت پيش‌پردازش‌هايي قرار گرفته، سپس 12 ويژگي که توصيف‌کننده‌هاي مناسبي از شکل، مرز و چگالي توده هستند، استخراج مي‌شوند. منحنی ROC و عملكرد آسيب‌شناسي حاصل از ترکيب تمام اين ويژگي‌ها توسط دو کلاسه‌بندي کننده با يادگيري متداول پس‌انتشار و يادگيري پيشنهادي OWBP ارزيابي شده و سيستم‌هاي حاصل از لحاظ سرعت يادگيري نیز مورد مقايسه قرار گرفته‌اند. همچنین در اين تحقيق قابليت آسيب‌شناسي هر گروه از ويژگي‌هاي شكل، مرز و چگالي به‌طور جداگانه بررسي شده است. پايگاه داده مورد استفاده در اين تحقيق MIAS است. سيستم نهايي پیشنهادی داراي Az 924/0، با سرعت يادگيري تقريباً 4 برابر سرعت يادگيري سيستم با کلاسه‌بندي کننده پس‌انتشار و همچنين عملکرد 86/92% مي‌باشد.
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