Examples Of Model Based Segmentation

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Model-based segmentation methods seek to transform this knowledge into intelligent algorithms that have prior knowledge about the structures of interest. These methods often consist of two steps. First, the location and the appearance of the primary model are initialized. Next, the shape and the appearance of the model are adjusted so that it is as close as possible to the dimensions measured in the image. An important feature considered for selecting a strategy when optimizing the shape of a model during segmentation is deformation. There are two main methods of deformation. One method addresses the deformation of the embedded space of the shape and the other is the modification of the parameters or degrees-of-freedom [15]. 1.4 Clustering…show more content…
Fuzzy clustering plays an important role in feature analysis, system identification and classifier design [21]. Given its capacity for managing uncertainty, impreciseness and vagueness, the fuzzy algorithm is far more realistic for solving real-world problems than hard clustering algorithms. The fuzzy C-mean (FCM) clustering algorithm [22] is a well-known method based on fuzzy clustering. In this method, the image is represented in different feature spaces and the FCM classifies similar data points according to the distance of the pixel from the center of the feature space. Related Works Many papers address image segmentation; however, this section only reviews papers that have tried to improve on FCM-based image segmentation. In its original design, the FCM algorithm assigns a membership value to each pixel and each image cluster. For image I with a grayscale range of for the ith pixel ( in k-dimensional space , the cluster centers are defined as . Parameter C denotes a positive value ( ) and is the membership value assigned to the ith pixel in the jth cluster. The respective objective function in FCM is defined as [23]: (1) where m represents the degree of fuzziness (m > 1) and is the Euclidean distance between the ith pixel and cluster center
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