ArticleComputational and mathematical methods in medicine2017
Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- Radiomics: a primer on high-throughput image phenotyping.Abdominal radiology (New York) · 2022Review
- Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images.Japanese journal of radiology · 2021Article
- Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.International journal of computer assisted radiology and surgery · 2020Article
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6 authors at 3 institutions in 1 country.
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Abstract
The hippocampus has been known as one of the most important structures referred to as Alzheimer's disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.
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