![]() ![]() It is still very challenging for existing methods to segment various types of cells automatically and robustly. The diversity of cells is growing rapidly and the demand for segmenting different types of cell images continue to increase in recent years. Experimental results showed that the proposed approach is very promising in meeting the strict accuracy requirements for many applications.read more read lessĪbstract: Research on cell segmentation is experiencing growing pains that need to be addressed for developing generic and robust techniques. Twelve types of cells are used to test the proposed approach in this paper. At last, we propose an area-constrained ultimate erosion method to separate the connected cells robustly. To get the optimal segmentation, we utilize the slope difference distribution based threshold selection method to segment the Gabor filtered gradient image. To improve the segmentation accuracy, we utilize the Gabor filter to increase the intensity uniformity of the gradient image. To this end, we utilize the gradients of cells instead of intensity for cell segmentation because the gradients are less affected by the global intensity variations. In this paper, we aim to propose a generic approach that is capable of segmenting various types of cells robustly and counting the total number of cells accurately. Because of the great diversity of cells, no traditional methods could segment various types of cells with adequate accuracy. The requirements for the segmentation accuracy are becoming stricter. We use four types of cells to verify the effectiveness of the proposed approach and the experimental results are favorable.Ībstract: Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. After cell detection, the proposed approach generates the timeline moving trajectory of a cell by connecting the cell positions along the time lapse image sequences based on morphological operations. In this paper, we propose a new approach of cell tracking by detection based on a multiple-threshold segmentation method that calculates multiple thresholds automatically and robustly. As a whole, these old threshold selection methods could not meet the accuracy requirement of cell detection adequately. Yet, the most frequently used segmentation technique by cell detection methods is still threshold selection that is determined manually or by algorithms proposed in the 1970s. TL DR: This paper proposes a new approach of cell tracking by detection based on a multiple-threshold segmentation method that calculates multiple thresholds automatically and robustly and uses four types of cells to verify the effectiveness.Ībstract: In recent years, cell tracking methods by detection have become more and more popular because they outperformed cell tracking methods by contour evolution in most practical cell tracking applications. ![]()
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