https://www.ncbi.nlm.nih.gov/pubmed/28006044
J Biomed Opt. 2016 Dec 1;21(12):126015. doi: 10.1117/1.JBO.21.12.126015.
Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging.
- 1
- Chosun University, Department of Computer Engineering, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of KoreabChosun University, Center for Holographic Imaging Informatics, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of Korea.
Abstract
The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.
핵심 장비, 알고리즘 : OFF-AXIS 디지털 홀로그래픽 현미경, NN (for Pattern Recognition)
적혈구를 2D 가 아닌 3D 로 인식해서, 뉴럴네트워크를 이용해 더 세분하게 잘 분류해서, 적혈구 관련 질병을 잘 찾아낸다.
2016 Human red blood cell recognition enhancement.pdf
