Spatial Fuzzy Clustering and Its Application for MRI and CT Image Segmentation

Abstract

Due to the low segmentation accuracy and sensitivity to initial contour in image segmentation of CV model, an image segmentation algorithm based on CV model combined with spatial fuzzy c-means was proposed for MRI and CT image segmentation with unclear boundary, artifact and high noise. Based on the rough segmentation of the image by using the fuzzy c-means clustering algorithm in the spatial domain, the initial contour is set by using the clustering information to assist the CV model, and the target region is segmented by iterative evolution. The experimental results showed that when the number of iterations was only 50, the Dice coefficient of our algorithm for segmentation of brain MRI images was 89.17%, 38.9% higher than the traditional CV model. It can be seen that the algorithm has higher discrimination and better segmentation effect for medical images.

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

“…e input image can always have a fixed size and then extract features from the entire image and apply it to MRI brain tumor clustering. e following problems will arise: First, the brain tumor image for clustering, a single pixel, must be classified, so the original input can only be the neighborhood of a single pixel, and the size of this neighborhood is difficult to grasp; secondly, different patients have different brain tumors and there are different image layers of brain tumors in the same patient [14]. Even if the neighborhood value of the original input layer is determined through the training layer, it is difficult to ensure that this neighborhood is suitable for all tumor points of this patient; third, how to make full use of the multimodal information of MRI to achieve the higher classification of accuracy.…”

“…In terms of feature extraction, although multimodal 3D-CNNs can extract the different information between the modalities that are more conducive to classification, deep learning will cause a partial loss of the original input information, and Haar wavelet transform is a simple and effective signal. e processing method is the preferred feature extraction method in pixel-based MRI brain tumor clustering [14]. erefore, while acquiring the features of multimodal 3D-CNNs, reference [15] uses the 3D neighborhood gray information, neighborhood mean, standard deviation, Haar wavelet low-frequency coefficients, and multimodal 3D CNNs of each modal MRI image.…”