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Computer vision based identification of abnormal tissues in biomedical images

机译:基于计算机视觉的生物医学图像异常组织识别

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Easy visualization and recognition of slight abnormality in the body of the human can be done through various methods of biomedical imaging. Abnormalities might be due to presence of tumor, also known as the group of abnormal cells that can directly destroy all healthy cells. In case of Brain these abnormal cells grows inside or around the brain. These abnormalities turn destructive and plays a determinative role in the quality of the health of the human and thus increasing the life expectancy and longevity. In early times the diagnosis of the tumors in brain was exhausting task as the symptoms that can be detected physically can only be seen in the advance stages of the tumor. In modern times imaging methods like Magnetic Resonance Imaging (MRI) provides efficient and meticulous insight of tumor condition. It supports the treatment at preliminary stage. In Digital Imaging and Communications in Medicines (DICOM) images, implementation of the image processing techniques help in the detection of the most minute cell with less probability of human error, better speed and high efficiency. Here the identification of abnormal tissue in biomedical images based on computer vision has been used. The features on which the abnormal and the normal images are differentiated are namely area, perimeter and entropy. Entropy has been extracted using the feature extraction methodology from Gray Level Co-occurrence Matrix (GLCM) of the sampled of tumor image.
机译:可以通过多种生物医学成像方法轻松地可视化和识别人体中的轻微异常。异常可能是由于肿瘤的存在,也被称为可以直接破坏所有健康细胞的异常细胞群。如果是大脑,这些异常细胞会在大脑内部或周围生长。这些异常现象具有破坏性,并在人类健康质量中起决定性作用,从而增加了预期寿命和寿命。在早期,脑部肿瘤的诊断工作非常繁琐,因为只能在肿瘤的晚期才能看到可以物理检测到的症状。在现代,诸如磁共振成像(MRI)的成像方法可提供对肿瘤状况的有效而细致的洞察。它支持初步治疗。在医学数字成像和通信(DICOM)图像中,图像处理技术的实施有助于以人为错误的可能性较小,速度更快和效率更高的方式检测最微小的单元格。在这里,已经使用了基于计算机视觉的生物医学图像中异常组织的识别。区分异常图像和正常图像的特征是面积,周长和熵。使用特征提取方法从肿瘤图像样本的灰度共生矩阵(GLCM)中提取熵。

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