Validity-guided (re)clustering with applications to image segmentation

Abstract

When clustering algorithms are applied to image segmentation, the goal is to solve a classification problem. However, these algorithms do not directly optimize classification quality. As a result, they are susceptible to two problems: P1) the criterion they optimize may not be a good estimator of "true" classification quality, and P2) they often admit many (suboptha€) solutions. This paper introduces an algorithm that uses cluster validity to mitigate P1 and P2. The validity-guided (re)clustering (VGC) algorithm uses cluster-validity information to guide a fuzzy (re)clustering process toward better solutions. It starts with a partition generated by a soft or fuzzy clustering algorithm. Then it iteratively alters the partition by applying (novel) splitand-merge operations to the clusters. Partition modifications that result in improved partition validity are retained. VGC is tested on both synthetic and real-world data. For magnetic resonance image (MRI) segmentation, evaluations by radiologists show that VGC outperforms the (unsupervised) fuzzy c-means algorithm, and VGC's performance approaches that of the (supervised) k-nearest-neighbors algorithm.

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

“…In addition, more complicated methods such as those described in 27,11 would be helpful in increasing the computational efficiency. Further performance improvement could be obtained by considering the grand mean and cluster scatter matrices within and between clusters in the unsupervised clustering 45,19,8,9,12,5,6,28,29,39,52.…”

“…(1) can be rewritten as (5) By applying the log-transform on both sides of Eqn. (1), denoting the log-transformed observed MR image data as y k , and the log-transformed true intensity of underlying tissues as x k , the MR image data can be approximated using the conventional manner as1,31 (6) where The estimation of bias field should be completed for each clustering iteration. Therfore, for efficiency, the choice of basis functions for approximating the bias field is important.…”

“…This could have a significant effect on the MRI segmentation. To deal with this problem, a few methods have been proposed in which some pre-selected seeds are included 5,6. Methods aimed at automating the selection of seeds were reviewed by Sucking et al 44.…”

A fuzzy modeling approach to cluster validity

“…a gaussian kernel N λ (l, j), taking values in [0,1] such that values within the block more or less contribute.…”

“…• The bi-dimensional artificial data set Bensaid [1] characterized by 3 classes of different cardinalities (6, 3 and 40).…”