Automatic segmentation of Leishmania parasite in microscopic images using a modified CV level set method

Visceral Leishmaniasis is a parasitic disease that affects liver, spleen and bone marrow. According to World Health Organization report, definitive diagnosis is possible just by direct observation of the Leishman body in the microscopic image taken from bone marrow samples. We utilize morphological and CV level set method to segment Leishman bodies in digital color microscopic images captured from bone marrow samples. Linear contrast stretching method is used for image enhancement and morphological method is applied to determine the parasite regions and wipe up unwanted objects. Modified global and local CV level set methods are proposed for segmentation and a shape based stopping factor is used to hasten the algorithm. Manual segmentation is considered as ground truth to evaluate the proposed method. This method is tested on 28 samples and achieved 10.90% mean of segmentation error for global model and 9.76% for local model.

[1]  Abdul Rahman Ramli,et al.  A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing , 2009, Biological Procedures Online.

[2]  Abbas Hussien Miry,et al.  Automatic Segmentation of Skin Lesions using Histogram thresholding , 2014, J. Comput. Sci..

[3]  P. Desjeux.  Leishmaniasis: current situation and new perspectives. , 2004, Comparative immunology, microbiology and infectious diseases.

[4]  Safar Ali Talari,et al.  Treatment of Cutaneous Leishmaniasis: Effectiveness, and Adverse Effects of the Drugs , 2005 .

[5]  K. Makhoul,et al.  Cutaneous leishmaniasis: recognition and treatment. , 2004, American family physician.

[6]  Yves Bourgault,et al.  Heart segmentation with an iterative Chan-Vese algorithm , 2008 .

[7]  Takeo Kanade,et al.  Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  Peter Meer,et al.  Unsupervised segmentation based on robust estimation and color active contour models , 2005, IEEE Transactions on Information Technology in Biomedicine.

[9]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[10]  Abderrahim Elmoataz,et al.  Graph-based tools for microscopic cellular image segmentation , 2009, Pattern Recognit..

[11]  Christian E. Schaerer,et al.  Mathematical morphology for counting Trypanosoma cruzi amastigotes , 2013, 2013 XXXIX Latin American Computing Conference (CLEI).

[12]  Sirish L. Shah,et al.  Automated and unsupervised detection of malarial parasites in microscopic images , 2011, Malaria Journal.

[13]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[14]  Huiyan Jiang,et al.  C-V level set based cell image segmentation using color filter and morphology , 2014, 2014 International Conference on Information Science, Electronics and Electrical Engineering.

[15]  Salem Saleh Al-amri.  Linear and Non-linear Contrast Enhancement Image , 2010 .

[16]  Nancy M. Salem,et al.  Segmentation of white blood cells from microscopic images using K-means clustering , 2014, 2014 31st National Radio Science Conference (NRSC).