Medical Image Segmentation based on Improved Fuzzy Clustering in Robot Virtual Surgical System
نویسندگان
چکیده
In view of the problems relating to the precision and convergence rate of traditional ant colony algorithm and fuzzy clustering algorithm on the medical image segmentation, a modified selfadaptive threshold ant colony optimization and fuzzy clustering (SAAF) algorithm were proposed here to realize the segmentation of the complex background medical image. As to the complex medical image, Otsu algorithm was firstly performed to obtain the optimal threshold, the local optimal solution of ant colony algorithm was avoided through the intervention of optimal threshold, then the clustering center and the number of cluster classes obtained through the selfadaptive threshold ant colony algorithm were imported into the fuzzy clustering algorithm until the image segmentation was finalized. The doctor can get the diseased organ by SAAF image segmentation algorithm according to the CT scanned images, and can use the segmented organ image to build the 3D virtual organ tissue model. Then, the doctor can achieve virtual surgical before using the real robot surgical to operate on patients. It will greatly improve the success rate of surgery and efficiency.
منابع مشابه
High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...
متن کاملRobust Potato Color Image Segmentation using Adaptive Fuzzy Inference System
Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...
متن کاملHigh Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...
متن کاملImage Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...
متن کاملCluster-Based Image Segmentation Using Fuzzy Markov Random Field
Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...
متن کامل