Current Proceedings on Technology

Yazarlar: Figen Ozen, Merve Ayyuce Kizrak

Konular:-

Anahtar Kelimeler:Biomedical image processing,Pattern recognition,Feature extraction,Kernel based methods

Özet: Leukemia is a type of cancer caused by abnormal increase of the white blood cells. Each year hundreds of thousands of people die of leukemia throughout the world. Leukemic cells become out of control and they spread independently. They cause structural and irreversible damage in the organs where blood cells are produced, in other organs and in tissues. If not treated properly, leukemia costs the life of the patient very quickly. Early diagnosis is vital and it should be followed by a treatment applied to the correct cells. It is possible to achieve successful results in treatment, if leukemic and non-leukemic cells are classified correctly. This work is focused on acute lymphocytic leukemia, which affects young children and has a higher expectation of survival rate as compared to acute myelogenous leukemia. A new algorithm that combines morphological image processing techniques with Kernel Ridge Regression is developed. The feature set is extracted using Gray Level Co-occurrence Matrices. The features used in classification are average, skewness, kurtosis, correlation, energy, cluster prominence and inverse-difference moment. The algorithm is tested on a large data set. The performance is measured based on the ensemble average and the root-mean-square error criteria. The results are compared with the diagnoses of the physician. Random runs are ensemble averaged and the success rates of the algorithm are found to lie in the range of 94.75-96.43%.

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