Wavelet-Based Multi-scale GVF Snake Model for Image Segmentation

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

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Multi-scale structured CNN with label consistency for brain MR image segmentation

Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization ◽

10.1080/21681163.2016.1182072 ◽

2016 ◽

Vol 6 (1) ◽

pp. 113-117 ◽

Cited By ~ 25

Author(s):

Siqi Bao ◽

Albert C. S. Chung

Keyword(s):

Image Segmentation ◽

Mr Image ◽

Multi Scale ◽

Mr Image Segmentation

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Underwater Sonar Image Segmentation Based on Snake Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.3675 ◽

2013 ◽

Vol 448-453 ◽

pp. 3675-3678

Author(s):

Jun Peng Wu ◽

Hai Tao Guo

Keyword(s):

Image Segmentation ◽

Greedy Algorithm ◽

Echo Signal ◽

Constraint Forces ◽

Shape Accuracy ◽

Snake Model ◽

Sonar Image ◽

Sonar Images ◽

Underwater Target ◽

Detection And Recognition

The underwater sonar image segmentation has been a topic of research for decades. Underwater sonar image is based on the interaction by the echo signal of sound toward the underwater objects or targets. Because of the serious noises polution and the dim target edge, the contrast and resolution of sonar images are obtaind in a decreased quanlity. This paper proposes an improved snake model that focuses on solving underwater target detection and recognition. According to the traditional snake model, it is defined as an energy minimizing spline which is influenced by external constraint forces, and it can guide the image forces to pull toward features, such as lines or edges. Compared with the traditional snake model, this snake model greedy algorithm can converge to the contours more quickly and more stably, especially in complex underwater environments. Examination of the results shows that using snake model greedy algorithm has a more clear shape accuracy.

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