Abstract
AbstractThis paper suggests a novel method for medical image segmentation using kernel based Atanassov's intuitionistic fuzzy clustering. The widely used fuzzy c means clustering that uses Euclidean distance has many limitations in clustering the regions accurately. To overcome these difficulties, we introduce a new method using Atanassov's intuitionistic fuzzy set theory that incorporates a robust kernel based distance function. As the membership degrees are not precise and may contain hesitation, Sugeno type fuzzy complement is used to find the non-membership values and then hesitation degree is computed. The algorithm uses all the three kernels – Gaussian, radial basis, and hyper tangent kernels. In the algorithm, for each pixel, two features are considered - pixel energy and mean and the average of the two features are taken. The method clusters the tumors/lesions/clots almost accurately especially in a noisy environment. Experiments are performed on several noisy medical images and to assess the perf...
Highlights
Clustering is an unsupervised segmentation where the image is segmented into different regions or groups
To show the efficacy of our method, our results are compared with i) conventional Fuzzy c-means (FCM), ii) intuitionistic fuzzy c means cluster, iii) kernel based fuzzy c means cluster (KFCM) algorithm, iv) K means clustering algorithm
It is observed that IFCM, IFCM with Gaussian kernel, and IFCM with radial basis kernel contain noise in the clustered images
Summary
Clustering is an unsupervised segmentation where the image is segmented into different regions or groups. Zhang and Song[8] proposed kernel based FCM incorporating a spatial term in the objective function Though these methods worked well on image segmentation but due to the incorporation of spatial terms, it takes much time in running the algorithm and it may be incorrect with images which are affected greatly by heavy noise. With the consideration of hesitation degree, intuitionistic fuzzy c means clustering was suggested by Chaira[10] where another function is introduced that is the intuitionistic fuzzy entropy in the objective function She segmented tumor/ abnormal lesions in CT scan brain images. An A-intuitionistic fuzzy clustering algorithm is proposed by incorporating three kernel distance functions such as Gaussian, radial basis, and hyper tangent. It is obvious that 0 ≤ π A (x) ≤ 1, for each x ∈ X
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