A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis
نویسندگان
چکیده
منابع مشابه
A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis
Multiple sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. Existing techniques try to reduce this bias by filling all lesions as normal-appearing white matter on T1-weighted images, considering each time-point separately. However, due to lesion segmentation errors and the presence of structures adjacent to the l...
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Due to their abnormal appearance, Multiple Sclerosis lesions can influence the results of various image analysis techniques such as segmentation and registration. As the multi-modal characteristic intensity of the Multiple Sclerosis lesions is different that of non-pathological tissues, a local multi-modal intensity similarity can be used to classify and segment lesions. In this work, lesions a...
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Skin cancer has been the most usual and illustrates 50% of all new cancers detected each year. If they detected at an early stage, treatment can become simple and economically. Accurate skin lesion segmentation is important in automated early skin cancer detection and diagnosis systems. The aim of this study is to provide an effective approach to detect the skin lesion border on a purposed imag...
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Skin cancer has been the most usual and illustrates 50% of all new cancers detected each year. If they detected at an early stage, treatment can become simple and economically. Accurate skin lesion segmentation is important in automated early skin cancer detection and diagnosis systems. The aim of this study is to provide an effective approach to detect the skin lesion border on a purposed imag...
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Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and...
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ژورنال
عنوان ژورنال: NeuroImage
سال: 2016
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2016.06.053