Comparative analysis of deep learning and graph cut algorithms for cell image segmentation

Abstract

Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into multiple regions, normally based on the characteristics of pixels in a given image. Image segmentation could involve separating the foreground from background in an image, or clustering image regions based on similarities in intensity, color, or shape. In this thesis, we consider the problem of cell image segmentation and evaluate the performance of two major techniques on a dataset of cell image sequences. First, we apply a traditional segmentation algorithm based on the so-called graph cut that addresses the segmentation problem using an energy minimization scheme defined on a weighted graph. Second, we use modern techniques based on deep neural networks, namely U-Net and LSTM that have a time-consuming training and a relatively quick testing phase. Performance of each technique will be analyzed qualitatively and quantitatively based on various standard measures and will be compared statistically.