Herold-Garcia S, Marrero-Fernández P, Guerrero-Peña F, Gual-Arnau X, Montoya-Pedrón A, Jaume-i-Capó A

Language: Spanish
References: 11
Page: 134-143
PDF size: 217.18 Kb.

ABSTRACT

Digital image processing and computer vision are frequently used in medicine at present and the proposals of new methods of automatic analysis of digital images or the efficiency improvement of the existing are of great interest. In this work new methods to computationally study sickle cell disease through blood samples images are developed, an illness with high incidence in the world and in Cuba, mainly in the eastern region. New shape analysis methods obtained from classical results of integral geometry and new computer vision proposals for evaluate neuro physiological disorders associated with this illness through the study of the facial expressions of the patient were proposed. The statistical validation realized confirmed the superiority of these methods on previous proposals, which is why they are valid to be introduced in support software to improve the quality of the medical attention.

REFERENCES

1. Espinosa E, Svarch E, Martinez G, Hernandez P. La anemia drepanocítica en Cuba: Experiencia de 30 años. Rev. Cuba. Hematol. Inmunol. Hemoter. 1996; 12(2): 97-105.

2. Wheeless L, Robinson R, Lapets OP, Cox C, Rubio A, Weintraub M, Benjamin LJ. Classfication of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature. Cytometry. 1994; 17(2): 159-166.

3. Frejlichowski D. Pre-processing, extraction and recognition of binary erythrocyte shapes for computer-assisted diagnosis based on MGG images. En: Bolc L, Tadeusiewicz R, Chmielewski LJ, Wojciechowski K, editors. ICCVG 2010: Proceedings or the International Conference on Computer Vision and Graphics. September 20-22, 2010, Warsaw, Poland; Springer, LNCS 6374 Part I, 2010: 368- 375.

4. Song H, Wang W. A new separation algorithm for overlapping blood cells using shape analysis. Int. J. Pattern Recog. Artif. Intell. 2009; 23(4): 847-864.

5. Kothari S, Chaudry Q, Wang MD. Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques. En: ISBI '09: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009 June 28 2009-July 1; Boston, MA: IEEE 2009: 795-798.

6. Kindratenko V. On using functions to describe the shapes. J. Math. Imaging Vision. 2003; 18: 225-245.

7. Delin R. Topics in Integral Geometry, World Scientific, Singapore, 1994.

8. Gual X, Herold S, Simó A. Shape description from generalized support functions. Pattern Recog Lett. 2013; 34: 619-626.

9. Gual X, Herold S, Simó A. Erythrocyte Shape Classification Using IntegralGeometry-Based Methods. Med Biol Eng Comput. 2015; 53(7): 623-633.

10. Gonzalez M, Guerrero FA, Herold S, Jaume-i-Capo A, Marrero PD. Red Blood Cell Cluster Separation from Digital Images for use in Sickle Cell Disease. IEEE J Biomed Health Inform. 2014; 19(4): 1514-1525.

11. Marrero P, Montoya A, Jaume-i-Capó A, Buades JM. Evaluating the Research in Automatic Emotion Recognition. IETE Tech Rev. 2014; 31(3): 220-232.