Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in ma...Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in magnetic resonance imaging (MRI), with MRI being one of the choices for evaluating the extent of osteosarcoma. However, it is still a challenge to automatically extract tumor from its surrounding tissues because of their low intensity differences in MRI. We investigated an approach based on Zernike moment and support vector machine (SVM) for osteosarcoma segmentation in T1-weighted image (TIWI). Firstly, the different order moments around each pixel are calculated in small windows. Secondly, the grayscale and the module values of different order moments are used as a texture feature vector which is then used as the training set for SVM. Finally, an SVM classifier is trained based on this set of features to identify the osteosarcoma, and the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on graphics processing unit (GPU). The performance of the method is validated on T1WI, showing that the segmentation method has a high similarity index with the expert's manual segmentation.展开更多
传统接触式甲烷泄漏传感器检测范围小且效率低,而结合非接触式红外热成像的机器视觉算法可实现远距离、大范围红外甲烷实例分割,对于提高甲烷检测效率及保障人员安全具有显著优势。然而远距离甲烷气体图像轮廓模糊、泄漏的甲烷气体与背...传统接触式甲烷泄漏传感器检测范围小且效率低,而结合非接触式红外热成像的机器视觉算法可实现远距离、大范围红外甲烷实例分割,对于提高甲烷检测效率及保障人员安全具有显著优势。然而远距离甲烷气体图像轮廓模糊、泄漏的甲烷气体与背景对比度较低且形状易受大气流动因素影响等问题限制了红外甲烷实例分割性能。针对上述问题,本文提出一种空间信息自适应调控和特征对齐的网络模型(Adaptive spatial information regulation and Feature alignment Network,AFNet)实现甲烷泄漏红外实例分割。首先,为增强模型的特征提取能力,提出自适应空间信息调控模块赋予主干网络不同尺度残差块自适应权重丰富模型提取的特征空间;其次,构建加权双向金字塔弥补特征金字塔自顶而下的特征传播方式导致的低层特征空间位置和实例边缘信息弥散丢失问题,以适应甲烷气体复杂轮廓变化下前景目标定位检测和轮廓分割需求。最后,设计原型特征对齐模块捕获长距离气体特征之间的语义关系丰富原型语义信息量以改善生成目标掩码质量提高甲烷气体分割精度。实验结果表明,本文提出的AFNet模型AP50@95,AP50定量分割精度分别达到42.42%,92.18%,相比于原始Yolact模型分割精度,分别提高9.79%,6.18%,推理速度达到36.80 frame/s,满足甲烷泄漏分割需求。实验结果验证了本文算法对红外甲烷泄漏分割的有效性和工程实用性。展开更多
文摘Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in magnetic resonance imaging (MRI), with MRI being one of the choices for evaluating the extent of osteosarcoma. However, it is still a challenge to automatically extract tumor from its surrounding tissues because of their low intensity differences in MRI. We investigated an approach based on Zernike moment and support vector machine (SVM) for osteosarcoma segmentation in T1-weighted image (TIWI). Firstly, the different order moments around each pixel are calculated in small windows. Secondly, the grayscale and the module values of different order moments are used as a texture feature vector which is then used as the training set for SVM. Finally, an SVM classifier is trained based on this set of features to identify the osteosarcoma, and the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on graphics processing unit (GPU). The performance of the method is validated on T1WI, showing that the segmentation method has a high similarity index with the expert's manual segmentation.
文摘传统接触式甲烷泄漏传感器检测范围小且效率低,而结合非接触式红外热成像的机器视觉算法可实现远距离、大范围红外甲烷实例分割,对于提高甲烷检测效率及保障人员安全具有显著优势。然而远距离甲烷气体图像轮廓模糊、泄漏的甲烷气体与背景对比度较低且形状易受大气流动因素影响等问题限制了红外甲烷实例分割性能。针对上述问题,本文提出一种空间信息自适应调控和特征对齐的网络模型(Adaptive spatial information regulation and Feature alignment Network,AFNet)实现甲烷泄漏红外实例分割。首先,为增强模型的特征提取能力,提出自适应空间信息调控模块赋予主干网络不同尺度残差块自适应权重丰富模型提取的特征空间;其次,构建加权双向金字塔弥补特征金字塔自顶而下的特征传播方式导致的低层特征空间位置和实例边缘信息弥散丢失问题,以适应甲烷气体复杂轮廓变化下前景目标定位检测和轮廓分割需求。最后,设计原型特征对齐模块捕获长距离气体特征之间的语义关系丰富原型语义信息量以改善生成目标掩码质量提高甲烷气体分割精度。实验结果表明,本文提出的AFNet模型AP50@95,AP50定量分割精度分别达到42.42%,92.18%,相比于原始Yolact模型分割精度,分别提高9.79%,6.18%,推理速度达到36.80 frame/s,满足甲烷泄漏分割需求。实验结果验证了本文算法对红外甲烷泄漏分割的有效性和工程实用性。
