طبقه‌بندی توده‌های سرطانی سینه با استفاده از ویژگی‌های ریخت‌شناسی توده و ويژگي‌هاي بافتی تصاویر سونوگرافی در ناحيه داراي توده و نواحي اطراف آن
محورهای موضوعی : مهندسی برق و کامپیوتررستمعلي جهان‌ديده 1 , حمید بهنام 2 * , نسرین احمدي‌نژاد 3
1 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
2 - دانشگاه علم و صنعت ایران
3 - مجتمع بیمارستانی امام خمینی (ره)
کلید واژه: سونوگرافيطبقه‌بندي سرطان سينهحذف اسپكلويژگي‌هاي ريخت‌شناسي و بافتي,
چکیده مقاله :
اولتراسوند يك ابزار تشخيصي بسيار مهم براي تفكيك توده‌هاي بدخيم و خوش‌خيم سرطان سينه مي‌باشد. تفسيرهايي كه بر روي تصاوير اولتراسوند انجام مي‌شود در برخي موارد دچار انحراف مي‌شود و خطاي انساني نمود پيدا مي‌كند. يك سيستم كمكي رايانه‌اي مي‌تواند براي متخصص نظر ثانويه‌اي را ايجاد كند و در طبقه‌بندي توده‌ها به دو گروه خوش‌خيم و بدخيم مؤثر باشد. در تحقيقات گذشته توانايي تحليل بافتي تصاوير سونوگرافي در طبقه‌بندي ضايعات نشان داده شده است. در تحليل‌هاي صورت‌گرفته ويژگي‌هاي بافتي ناحيه داراي توده مد نظر بوده است در حالي كه مفسران تصاوير اولتراسوند براي تشخيص نوع توده، نواحي اطراف توده را نيز مورد توجه ويژه قرار مي‌دهند. با توجه به اين مسئله در اين مطالعه به بررسي ويژگي‌هاي مؤثر اطراف توده در تشخيص نوع توده پرداخته‌ايم. از اين رو چهار ويژگي بافتي از سه ناحيه (ناحيه داراي توده، ناحيه پشت توده و ناحيه هم‌عمق و مجاور ناحيه پشت توده) به‌همراه ويژگي‌هاي ريخت‌شناسي توده مورد بررسي قرار گرفت. اين ويژگي‌ها در شش حالت تركيبي مورد بررسي قرار گرفتند و نتايج معناداري حاصل شد. براي طبقه‌بندي از ابزار شبكه عصبي نوع MLP استفاده شد. پايگاه داده‌ها با 36 تصوير شكل گرفت كه نتايج تشخيص آنها توسط آزمايش‌هاي پاتولوژيك به‌صورت 18 تصوير خوش‌خيم و 18 تصوير بدخيم مورد تأييد قرار گرفت.
Ultrasonography is one of the most useful diagnostic tools for human soft tissue and is one of the methods that are in routine use for distinguishing benign and malignant breast tumors. But its diagnosis is operator dependent. In previous researches texture analysis for solid breast mass classification is used. In those works texture features of the tumor are used, but sonologists notice to the features of the surrounding area of the tumors for their diagnosis. In this research as well as the morphological features of the mass the features of the surrounding area of the mass are also considered. MLP neural network is used for classification. 36 breast sonography images are used that 18 of them proved to be benign and 18 of them proved to be malignant through biopsy. The features are used in different combinations and it is shown that using the texture features of behind the tumor area and the same depth near the tumor provide meaningful result and also compensate the different adjustments of the systems.
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