中和 发表于 2025-3-26 22:26:33
Probabilistic Segmentation of Brain White Matter Lesions Using Texture-Based Classification of these lesions is typically performed manually by physicians on magnetic resonance images and represents a non-trivial, time-consuming and subjective task. The proposed method automatically segments white matter lesions using a probabilistic texture-based classification approach. It requires no pFLASK 发表于 2025-3-27 04:35:36
http://reply.papertrans.cn/47/4614/461392/461392_32.pngABASH 发表于 2025-3-27 06:56:15
Ejection Fraction Estimation Using a Wide Convolutional Neural Networkutional neural network to localize the left ventricle from MRI images. Then, the systole and diastole images can be determined based on the size of the localized left ventricle. Next, the network is used in order to segment the region of interest from the diastole and systole images. The end systoli实现 发表于 2025-3-27 13:14:29
Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weightering and detecting prostate tumors. The clinical guidelines to interpret DW-MRI for prostate cancer requires the segmentation of the prostate gland into different zones. Moreover, computer-aided detection tools which are designed to detect prostate cancer automatically, usually require the segmentatNIL 发表于 2025-3-27 14:18:26
http://reply.papertrans.cn/47/4614/461392/461392_35.pngCORD 发表于 2025-3-27 20:39:36
http://reply.papertrans.cn/47/4614/461392/461392_36.png碎石 发表于 2025-3-27 23:46:31
Object Boundary Based Denoising for Depth Images suffers from depth image noise. This paper proposes a solution to the problem by estimating depth edges that correspond to the object boundaries and using them as priors in the hole filling process. This method exhibits quantitative and qualitative improvements over the current state-of-the-art metlavish 发表于 2025-3-28 02:56:36
http://reply.papertrans.cn/47/4614/461392/461392_38.pngCeliac-Plexus 发表于 2025-3-28 09:31:02
http://reply.papertrans.cn/47/4614/461392/461392_39.png永久 发表于 2025-3-28 12:41:18
Image Analysis and Recognition978-3-319-59876-5Series ISSN 0302-9743 Series E-ISSN 1611-3349