An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders
提出一种基于拉普拉斯高斯算子改进、边界开放快速径向对称种子点检测和混合椭圆拟合的血细胞分割方法，在噪声抑制和分割精度上优于现有技术，适用于血液疾病诊断。
The automatic segmentation of blood cells for detecting hematological disorders is a crucial job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing methods suffer from the issues like noise, improper seed-point detection, and oversegmentation problems, which are solved here using a Laplacian-of-Gaussian (LoG)-based modified highboosting operation, bounded opening followed by fast radial symmetry (BOFRS)-based seed-point detection, and hybrid ellipse fitting (EF), respectively. This article proposes a novel hybrid EF-based blood-cell segmentation approach, which may be used for detecting various hematological disorders. Our prime contributions are: 1) more accurate seed-point detection based on BO-FRS; 2) a novel least-squares (LS)-based geometric EF approach; and 3) an improved segmentation performance by employing a hybridized version of geometric and algebraic EF techniques retaining the benefits of both approaches. It is a computationally efficient approach since it hybridizes noniterative-geometric and algebraic methods. Moreover, we propose to estimate the minor and major axes based on the residue and residue offset factors. The residue offset parameter, proposed here, yields more accurate segmentation with proper EF. Our method is compared with the state-of-the-art methods. It outperforms the existing EF techniques in terms of dice similarity, Jaccard score, precision, and F1 score. It may be useful for other medical and cybernetics applications.