A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

Abstract

This paper aims to make a review on the current segmentation algorithms used for medical images.Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused due to the intensive investigations on the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarized. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.

Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

“…In contrast, region-based algorithms were widely incorporated with other algorithms, including the Woodcock and Harward [20] region growing algorithm, eCognition's imagery merging and fractal net evolution approach [21,23,24], and multiscale object-specific segmentation (MOSS) using size constrained region merging [21,40]. Those algorithms could better deal with noise and avoid over-segmentation on vegetation mapping, compared to other algorithms, such as the watershed algorithm and the region growing algorithm [34,37,41]. However, two-step labeling procedures [30] or repetitive testing on parameters (e.g., scale, compact and shape parameters for multiresolution segmentation in eCognition) to construct hierarchical frameworks [21][22][23][24] were still time-consuming and data-intensive.…”

“…Previous studies emphasized intensive data requirements [25,26,[28][29][30] and repetitive testing on the applications of vegetation mapping, but those approaches may not form robust procedures toward long-term image processing with uncertain data sources (i.e., validation points) and the lack of multiple attributes. Moreover, segmentation studies (reviewed by [32][33][34][35][36][37]) have not been extensively applied to natural vegetation using a robust approach. Among those segmentation methods, which were classified by Fu and Mui [32], edge-based algorithms were the most vulnerable to noise (i.e., heterogeneous pixels with higher variances, especially in sparse tree stands) [32], and threshold-based algorithms cannot deal with imagery complexities, even using local threshold approaches [38,39].…”

Online chromatic and scale-space microvessel-tracing analysis for transmitted light optical images

“…Therefore, sharp features will be detected at fine scales, that is, those that have been slightly smoothed, whilst coarse features will be detected with considerable smoothing of the images. Both algorithms are "intensity-based" as opposed to clustering or deformable models (see (Ma et al, 2010) for a review of Medical Image Segmentation techniques), and the scale-space algorithm belongs to the "Ridge detection -Pattern Recognition" techniques (see (Kirbas and Quek, 2004) for a review of Vessel Extraction Techniques).…”

Extraction of Brain Tumour in MRI Images using Marker Controlled Watershed Transform Technique in MATLAB

“…Image processing in medical science is significantly applied to magnetic resonance (MR) as images of the brain. Image segmentation is given much consideration and care during the last twenty years [23][24][25][26]. Biomedical images are formed by objects of varying shapes, sizes and intensities [1]- [4].…”