Edge-based active contour methods are popular algorithms for image segmentation, with the purpose to extract the area of interest. However, they may face to boundary leakage and improper segmentation when handle images under weak edges or complex shapes. The extensive edge-stop functions adopt edge information, which cannot apply to guide the evolving curve approaching to target boundaries. To resolve this issue, a novel level set algorithm based on non-local means (NLM) filtering is constructed in this study. Firstly, the images are subjected to non-local means filtering to generate edge map. Secondly, a new edge-stop function constructed from this edge map as well as the fuzzy k-NN classification algorithm is incorporated into the variational model. Our experiments demonstrate that non-local means filtering is able to sharp edges both on medical and natural images. Thus, this analysis seems to be useful for clinical medical diagnosis.
Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises. INDEX TERMS Active contour, image segmentation, signed pressure force, level set.
Pavement crack condition is a vitally important indicator for road maintenance and driving safety. However, due to the interference of complex environment, such as illumination, shadow and noise, the automatic crack detection solution cannot meet the requirements of accuracy and efficiency. In this paper, we present an extended version of U-Net framework, named MSK-UNet, for pavement crack to solve these challenging problems. Specifically, first, the U-shaped network structure is chosen as the framework to extract more hierarchical representation. Second, we introduce selective kernel (SK) units to replace U-Net’s standard convolution blocks for obtaining the receptive fields with distinct scales. Third, multi-scale input layer establishes an image pyramid to retain more image context information at the encoder stage. Finally, a hybrid loss function including generalized Dice loss with Focal loss is employed. In addition, a regularization term is defined to reduce the impact of imbalance between positive and negative samples. To evaluate the performance of our algorithm, some tests were conducted on DeepCrack dataset, AsphaltCrack300 dataset and Crack500 dataset. Experimental results show that our approach can detect various crack types with diverse conditions, obtains a better performance in precision, recall and [Formula: see text]-score, with 97.43%, 96.95% and 97.01% precision values, 82.51%, 93.33% and 87.58% recall values and 95.33%, 99.24% and 98.55% [Formula: see text]-score values, respectively.
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