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Image Segmentation Using an Adaptive Clustering Technique for the Detection of Acute Leukemia Blood Cells Images

机译:使用自适应聚类技术检测急性白血病血细胞图像的图像分割

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Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting the blood cells in the early of prognosis. This paper aims to segment the blood cell images of patients suffering from acute leukemia using an adaptive K-Means clustering together with mean shift algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene.
机译:聚类是许多字段中使用的最常见的自动图像分段技术之一,包括机器学习,模式识别,图像处理和生物信息学。最近,许多科学家在帮助血液医生在预后早期分割血细胞的问题中表现出巨大的研究。本文旨在将患者的血细胞图像分段使用自适应k均值与平均移位算法一起进行急性白血病患者。集成的聚类技术已经产生了具有最小过滤过程的全面输出图像,以删除背景场景。

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