Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

Palenichka, Roman M.; Lakhssassi, Ahmed; Zaremba, Marek B.

doi:10.1016/j.engappai.2011.03.004

Cited by 2 publications

(1 citation statement)

References 28 publications

(32 reference statements)

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“…A Multi-scale segmentation (Palenichka et al, 2011) of surface data using scale-adaptive region growing is proposed and the performance of this method was evaluated on LiDAR surface images. The authors proposed segmentation algorithm, which is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a twocriterion score function.…”

Section: Discussionmentioning

confidence: 99%

A Review on Recent Research and Implementation Methodologies on Medical Image Segmentation

Somasundaram¹

2012

Journal of Computer Science

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Problem statement: Image segmentation plays a very important role in digital imaging applications for classifying the anatomical structures and other Regions of Interest. Segmentation refers to the process of identifying and isolating the surface and regions of the digital image which corresponds to the structural units. Approach: In the past few decades, many methods have been proposed to segment the digital image. Most of them were based on two basic properties of the pixels in relation to their local neighborhood: discontinuity and similarity. The approaches based on discontinuity partition of an image are performed by detecting isolated points, lines and edges, which were known as edge detection techniques. Results: A particular class of segmentation techniques is based on multi resolution analysis. The image segmentation process is includes various techniques like Threshold method, boundary tracking, Clustering, Region-Based. Conclusion: The objective of this study is to provide brief study on various segmentation methodologies for the researchers. This study presents the brief review on image segmentation and analyses the performance and improvement of various algorithms and fulfilled the objectives.

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Section: Discussionmentioning

confidence: 99%