This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

No abstract

No abstract

Image segmentation is a fundamental step in several image processing tasks. It is a process where an image is divided into its constituent regions guided by a similarity criterion. One very interesting image segmentation method is the color structure code (CSC), which combines simultaneously split-and-merge and region-growing techniques. In this paper, a segmentation approach based on the CSC method, weighted color structure code (WCSC), is proposed. This method is guided by a nonlinear discrimination function, where the user-inference is captured by the Polynomial Mahalanobis distance, prioritizing, during the merging process, the regions with higher similarity to the user selected pattern. The WCSC has color distribution pattern-oriented characteristic, showing better coherence among the segments with higher similarity to the selected pattern. A qualitative evaluation and parametric paired analysis were performed to compare CSC, WCSC and other segmentation methods results, using images from Berkeley benchmark. The results from these comparison indicate an improvement on the segmentation result obtained by the WCSC.

We present a new segmentation method called weighted Felzenszwalb and Huttenlocher (WFH), an improved version of the well-known graph-based segmentation method, Felzenszwalb and Huttenlocher (FH). Our algorithm uses a nonlinear discrimination function based on polynomial Mahalanobis Distance (PMD) as the color similarity metric. Two empirical validation experiments were performed using as a golden standard ground truths (GTs) from a publicly available source, the Berkeley dataset, and an objective segmentation quality measure, the Rand dissimilarity index. In the first experiment the results were compared against the original FH method. In the second, WFH was compared against several well-known segmentation methods. In both case,s WFH presented significant better similarity results when compared with the golden standard and segmentation results presented a reduction of over-segmented regions.