Accuracy Improvement of Graph-Cut Image Segmentation by Using Watershed

Rong, Jian; Pan, Yu Li

doi:10.4028/www.scientific.net/amr.341-342.546

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…A common method based on region, the image starts from a certain point and merges the surrounding pixel points with the same attribute (including gray value, texture and other features) (47). There are also some segmentation algorithms based on specific theories, such as minimized graph cut algorithm based on energy (48), conditional random field method based on statistics (49), and clustering analysis method based on fuzzy sets (50), etc. In addition, the deep learning network can automatically obtain features from the training data and achieve good segmentation performance (51,52).…”

Section: Machine Learning Modementioning

confidence: 99%

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Qin

Wang

et al. 2022

Front. Neurol.

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Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.

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Section: Machine Learning Modementioning

confidence: 99%

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Qin

Wang

et al. 2022

Front. Neurol.

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Landform classification based on landform geospatial structure – a case study on Loess Plateau of China

Lin

Xie

Deng

et al. 2022

International Journal of Digital Earth

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Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models

Lin

Chen

2021

Remote Sensing

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Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.

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Accuracy Improvement of Graph-Cut Image Segmentation by Using Watershed

Cited by 3 publications

References 5 publications

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Landform classification based on landform geospatial structure – a case study on Loess Plateau of China

Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models

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