Görüntü Bölütleme için Fourier Dönüşümü, Hessian Matris ve Özdeğerler Kullanılarak Yeni bir Aktif Kontur Modeli

Aktif kontur model nesne sınırlarını bölütleyebilir ve bu yüzden görüntü analizinde ve bölütlemesinde kullanılmaktadır. Frekans bilgisi ve yüksek mertebe diferansiyel hesaplamalar içermeyen mevcut aktif kontur modelleri yoğunluk eşitsizliği ve gürültü içeren bazı görüntüleri bölütlerken başarısızdır. Bu çalışmada mevcut Hessian matris ve özdeğer temelli metot içerisine Fourier dönüşümü entegre edilerek yeni bir aktif kontur modeli geliştirilmiştir. Giriş görüntüsünün Fourier dönüşümü hesaplanmış ve düzey küme fonksiyonunda aktif bir şekilde kullanılmıştır. Sonuçta frekans alanında elde edilen piksel yoğunluk bilgisinin diferansiyel analizi gerçekleştirilmiştir. Ayrıca piksel analizinin uzaysal bilgi içerdiği mevcut Hessian matris ve özdeğer temelli metottan farklı olarak, bu yeni model değişmez Fourier alanında sınır piksel bileşenlerini tespit etmeyi amaçlamaktadır. Geliştirilen model mevcut Hessian matris ve özdeğer temelli metot ve LIF metodu ile karşılaştırılmıştır. Deneysel sonuçlar önerilen metodun düşük iterasyon ve yüksek bölütleme doğruluğu ile daha iyi bölütleme performansını elde edebildiğini göstermiştir.

Anahtar Kelimeler:

Aktif kontur modeli, Görüntü bölütleme, Fourier dönüşümü, Hessian matrisi

A New Active Contour Model Using Fourier Transform, Hessian Matrix And Eigenvalues For Image Segmentation

Active contour model can segment object boundaries and thus it has been used in image analysis and segmentation. Current active contour models without frequency information and high-order differential computations fail when segmenting some images containing intensity inhomogeneity and noise. In this paper, a new active contour model has been developed by integrating the Fourier transform into the existing Hessian matrix and eigenvalue-based method. The Fourier transform of the input image is calculated and used actively in the level set function. As a result, differential analysis of the pixel density information obtained in the frequency domain has also performed. Also, unlike the current Hessian matrix and eigenvalue-based method where the pixel analysis contains the spatial information, this new model aim to detect the boundary pixel components in the invariant Fourier domain. The developed method is compared with existing Hessian matrix and eigenvalue-based method and LIF method. Experimental results show that the proposed method can achieve a better segmentation performance with less iterations and high segmentation accuracy.

Keywords:

Active contour model, Image segmentation, Fourier transform, Hessian matrix,

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