Mamografi Görüntülerinde Dışbükey Kitle Şekillerinin Hızlı Fourier Dönüşümünü (FFT) ve Zernike Momentlerini Kullanarak Kitle Algılama

Year 2021, , 738 - 752, 31.12.2021

Hatice Aydın , Semih Ergin

https://doi.org/10.35193/bseufbd.861211

Abstract

Bu çalışmada, mamografi görüntülerinde şüpheli bölge olarak tanımlanan bölgelerin dışbükey kitle sınırının Zernike momentlerinden ve Hızlı Fourier Dönüşümünden (FFT) faydalanarak kitle algılama uygulaması geliştirilir. Uygulamanın geliştirilmesi sırasında araştırmacıların kullanımına açık olan Mammografi Analiz Topluluğu (MIAS) veritabanı kullanılır. MIAS veritabanı, 322 adet 1024x1024 piksel çözünürlüklü normal, iyi huylu ve kötü huylu kanser mamografi görüntülerini içerir. Çalışmanın ilk aşamasında, görüntüler üzerinde gürültü azaltma ve görüntü iyileştirme işlemi yapılmaktadır. Şüpheli bölgelerle benzer özelliklere sahip olan pektoral kaslar görüntülerden ayrıştırılır. Ayrıştırma işleminden sonra, şüpheli bölgeleri netleştirmek için görüntüler kontrast yönünden iyileştirilir. Şüpheli bölgelerden, dışbükey kitle sınırının Zernike momentleri ve FFT'si hesaplanır ve her görüntü için öznitelik vektörleri elde edilir. Her bir görüntünün yeni öznitelik vektörü eğitim ve test kümelerine ayrılmış ve test kümesinin etiketleri %100 doğruluk ile elde edilmiştir.

Keywords

Meme Kitlesi, Pektoral Kas Ayrıştırma, Destek Vektör Makinası, Doğrusal Ayırma Analizi

Mass Detection Using the Zernike Moments and Fast Fourier Transform (FFT) of Convex Mass Shapes on Mammogram Images

Abstract

In this study, mass detection application is developed for mammograms from Zernike moments and Fast Fourier Transform (FFT) of convex mass boundary. During the development of the application, the Mammographic Image Analysis Society (MIAS) database, which is available to the researchers, is used. The MIAS database contains 322, 1024x1024 pixel resolution images of normal, benign, and malignant cancer. In the first phase of the study, noise reduction and image enhancement process is performed on the images. The pectoral muscles, which have similar features as region of interests (ROIs) are decomposed. After the decomposition process, images are enhanced by contrast to clarify ROIs. From ROIs, Zernike moments and FFT of convex mass boundary are calculated and feature vectors are obtained for each image. The new feature vector of each image was divided into training and test sets, and the labels of the test set were obtained with 100% accuracy.

Keywords

Breast Mass, Pectoral Muscle Removal, Support Vector Machine, Linear Discriminant Analysis

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