A segmentation method for images compressed by fuzzy transforms

被引：46

作者：

Di Martino, Ferdinando ^{[1
,2
]}

Loia, Vincenzo ^{[2
]}

Sessa, Salvatore ^{[1
]}

机构：

[1] Univ Naples Federico II, Dipartimento Costruz & Metodi Matemat Architettur, I-80134 Naples, Italy

[2] Univ Salerno, Dipartimento Matemat & Informat, I-84084 Salerno, Italy

来源：

FUZZY SETS AND SYSTEMS | 2010年 / 161卷 / 01期

关键词：

FGFCM; PCAES; Fuzzy relation; Similarity measure; Fuzzy transform; Image segmentation; Lukasiewicz t-norm; PSNR; CLUSTER-VALIDITY; ALGORITHMS;

D O I：

10.1016/j.fss.2009.08.002

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we describe a segmentation method applied to images which are compressed by using Fuzzy Transforms. The segmentation of the images is realized via the FGFCM (Fast Generalized Fuzzy C-Means) clustering algorithm, which is robust to noise and outliers. The optimal number of clusters is determined via the PCAES (Partition Coefficient And Exponential Separation) validity index. We use a similarity measure defined via Lukasiewicz t-norm for comparison between the original image and the reconstructed images. The best results are obtained if this similarity measure overcomes a threshold value, experimentally determined from the analysis of the trend of it with respect to the PSNR (Peak Signal to Noise Ratio). (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：56 / 74

页数：19

共 51 条

[1] A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data [J].