In this work, we propose a new segmentation algorithm for images containing convex objects present in multiple shapes with a high degree of overlap. The proposed algorithm is carried out in two steps, first we identify the visible contours, segment them using concave points and finally group the segments belonging to the same object. The next step is to assign a shape identity to these grouped contour segments. For images containing objects in multiple shapes we begin first by identifying shape classes of the contours followed by assigning a shape entity to these classes. We provide a comprehensive experimentation of our algorithm on two crystal image datasets. One dataset comprises of images containing objects in multiple shapes overlapping each other and the other dataset contains standard images with objects present in a single shape. We test our algorithm against two baselines, with our proposed algorithm outperforming both the baselines.
One of the popular approaches for segmentation of overlapping objects is the watershed algorithm @cite_13 which in its classical form often results in over-segmentation. A potential solution to over-segmentation is region merging, which does solve the problem but the method efficiency varies depending upon the object size and object distribution concentration in the image. Marker-controlled watershed @cite_15 @cite_4 is also used for the purpose but the efficiency of the method depends highly on the accurate identification of the markers.
@cite_15 @cite_13 @cite_4
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Cluster division is a critical issue in fluorescence microscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking clustered nuclei and analyzing only isolated nuclei may dramatically increase analysis time or affect the statistical validation of the results. Automatic segmentation of clustered nuclei requires the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of internuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to correctly segment most clustered nuclei, or perform well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of microscopic nuclei clusters. This algorithm provides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, more importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90 of the test clusters were correctly segmented in peripheral blood and bone marrow preparations. The algorithm was valid for both types of samples, using the appropriate markers and transformations. Cytometry 28:289–297, 1997. © 1997 Wiley-Liss, Inc. A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided. > It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8 segmentation accuracy, 97.4 cell division tracking accuracy, and 97.6 cell tracking accuracy