# Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets
## 2 Related Work
In [40], a self-counting mechanism is proposed for flagellate trypanosomes infecting human cells: Firstly, morphological pre-treatment removes complex image backgrounds; secondly, unsupervised classification is used to segment collections; thirdly, threshold processing is used to preserve infected cells; and finally, cells are processed by morphological treatment and filtered by average. In [41], a robust segmentation method is proposed for counting milk somatic cells on a microscope slide image. By changing the original RGB(Red-Green-Blue, three primary colors) format of the image to the lab color space and applying the k-means clustering algorithm to remove debris and other background features, a new gray-level threshold processing method is proposed, and the remaining boundary units are separated in the final segmentation step of applying the watershed transform. In [42], a new approach is proposed using powerful cell detection and online dictionary learning for automatic Ki-67 counting. The state-of-the-art research on target detection and recognition based on clustering features can be summarized as follows: In [15], a block-based color cluster background modeling and a foreground detection algorithm that possesses efficient processing and low memory requirement in a complex scene was proposed. In training phase, the color cluster and pixel distribution line (PDL) are efficiently used to reduce the background information. In the detection phase, the foreground objects were extracted precisely and fast in a complex scene by comparing the color clusters and PDLs. In [16], a salient object detection (SOD) approach was proposed to automatically isolate important objects from the background in images. In [17], a subcategory-aware CNN (S-CNN) was proposed to solve the object intra-class variation problem. In the proposed technique, the training samples were first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component aggregated channel feature (ACF) detector was then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels were further fed into a CNN classifier to filter out false proposals for object detection. In addition, an iterative learning algorithm was designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. In [18], a scalable bi-level parallel object detection framework based on heterogeneous manycore cluster was established to improve object detection performance for embedded device. First, the fundamental principle of local binary pattern and cascade classifier combined object-detection method was introduced as the basis of the research. Second, a set of key algorithms designed to parallel access and process image for object detection based on Parallella manycore platform was proposed to improve the detection speed and the computational resource efficiency on a single node. Third, a message passing interface-based distributed framework was established for cluster environment to further improve the performance. Finally, an experiment of face detection application was conducted to evaluate the accuracy and performance of this framework. Due to the size difference and shape non-fixation of cardiomyocytes, and the difficulty of cardiomyocytes motion blur and high noise in experimental image acquisition, the above algorithms cannot accurately count and cluster cardiomyocytes. The main contribution of this paper is the design and implementation of a new real-time automatic counting based on cardiomyocyte morphology and clustering system for cardiomyocytes based on hesitant fuzzy sets. This algorithm can quickly let the experimenter know the number of currently viable cardiomyocytes in the culture medium and the current morphological changes of these cardiomyocytes.