Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

Abstract

In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step. The majority of methods are not able to give an integrated structure for finding fast and effective tumor segmentation. Hence segmentation of tumor is most difficult task in diagnosing. In this paper, CT abdominal image is segmented using Superpixel-based fast Fuzzy C Means clustering algorithm to decrease the time needed for computation and eradicate the manual interface. In this algorithm, a superpixel image with perfect contour can be obtain using a Multiscale morphological gradient reconstruction operation. Superpixel is presegmentation algorithm and is employed to obtain segmentation accuracy. FCM with modified object is used to obtain the color segmentation. This method is examined on 20 CT images gathered from liveratlas database, results shows that this approach is fast and accurate compared to most of segmentation algorithms. Statistical parameters which include accuracy, precision, sensitivity, specificity, dice, rfn and rfp are calculated for segmented image. The results shows that this algorithm gives high accuracy of 99.58% and improved rfn value of 8.34% compared with methods discussed in the literature.

A Critical Review on Machine Learning based Liver Tumor Classification

“…This method is accurate and effective for many types of tumors. Munipraveena rela et al [15], utilized a superpixelbased FFCM clustering for LT segmentation. To obtain segmentation accuracy, multiscale morphological gradient reconstruction based superpixel is used.…”

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

“…On the other hand, superpixel can reduce the myriad of separate pixels in an image by replacing every pixel in a segment with the superpixel region's mean value [ 52 , 53 ]. Recently, we demonstrated a random forest (RF) and deep convolutional network-based segmentation method and have used multi-scale superpixels features for pathological lung CT image segmentation [ 54 ]. They evaluated performance based on statistical similarity indexes as parameters.…”

“…Text Segmentation [1] Region Growing [12,13] Fuzzy connectedness [28] Graph Cut [27,28] Random Walk [28,29] Watershade [30,31] Hard Clustering [35][36][37][38] Soft Clustering [39][40][41][42] C-Means [45][46][47][48][49] K-Means [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] Fusion Based…”

Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators

“…In [12] computer diagnosis of tumors is important, as their segmentation is difficult to diagnose. The Fuzzy K Means fast clustering algorithm based on super pixels was used.…”