کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6853389 | 1437155 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Medical image classification based on multi-scale non-negative sparse coding
ترجمه فارسی عنوان
طبقه بندی پزشکی بر اساس چندین مقیاس کد گذاری ناقص غیر منفی
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کلمات کلیدی
طبقه بندی پزشکی تصویر، فاصله معنایی، تجزیه چند مقیاس، برنامه نویسی ناقص منفی، تجزیه و تحلیل تجزیه و تحلیل،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence in Medicine - Volume 83, November 2017, Pages 44-51
Journal: Artificial Intelligence in Medicine - Volume 83, November 2017, Pages 44-51
نویسندگان
Ruijie Zhang, Jian Shen, Fushan Wei, Xiong Li, Arun Kumar Sangaiah,