1 research outputs found
Evolving Fuzzy Image Segmentation with Self-Configuration
Current image segmentation techniques usually require that the user tune
several parameters in order to obtain maximum segmentation accuracy, a
computationally inefficient approach, especially when a large number of images
must be processed sequentially in daily practice. The use of evolving fuzzy
systems for designing a method that automatically adjusts parameters to segment
medical images according to the quality expectation of expert users has been
proposed recently (Evolving fuzzy image segmentation EFIS). However, EFIS
suffers from a few limitations when used in practice mainly due to some fixed
parameters. For instance, EFIS depends on auto-detection of the object of
interest for feature calculation, a task that is highly application-dependent.
This shortcoming limits the applicability of EFIS, which was proposed with the
ultimate goal of offering a generic but adjustable segmentation scheme. In this
paper, a new version of EFIS is proposed to overcome these limitations. The new
EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to
self-estimate the parameters that are fixed in EFIS. As well, the proposed
SC-EFIS relies on a feature selection process that does not require
auto-detection of an ROI. The proposed SC-EFIS was evaluated using the same
segmentation algorithms and the same dataset as for EFIS. The results show that
SC-EFIS can provide the same results as EFIS but with a higher level of
automation.Comment: Benchmark data (35 breast ultrasound images with gold standard
segments) available; 11 pages, 4 algorithms, 6 figures, 5 tables