Current Proceedings on Technology

Yazarlar: Evaldas Vaiciukynas, Antanas Verikas, Adas Gelzinis Department, Marija Bacauskiene, Sigitas Sulcius, Ricardas Paskauskas, Irina Olenina

Konular:-

Anahtar Kelimeler:Contour detection,Level set,Trainable segmentation,Differential evolution,Quadratic-Chi distance

Özet: Novel prototype-based framework for image segmentation is introduced and successfully applied for cell segmentation in microscopy imagery. This study is concerned with precise contour detection for objects representing the Prorocentrum minimum species in phytoplankton images. The framework requires a single object with the ground truth contour as a prototype to perform detection of the contour for the remaining objects. The level set method is chosen as a segmentation algorithm and its parameters are tuned by differential evolution. The fitness function is based on the distance between pixels near contour in the prototype image and pixels near detected contour in the target image. Pixels “of interest correspond to several concentric bands of various width in outer and inner areas, relative to the contour. Usefulness of the introduced approach was demonstrated by comparing it to the basic level set and advanced Weka segmentation techniques. Solving the parameter selection problem of the level set algorithm considerably improved segmentation accuracy.

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