Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest...

Acute lymphoblastic leukemia (ALL) is the most frequently leukemia and categorized into three morphological subtypes named L1, L2 and L3. Early diagnosis of ALL plays a key role in treatment procedure especially in the case of children. Several similarities between morphology of three subtypes ALL (L1, L2, L3) and lymphocyte subtypes (normal, reactive and atypical) as noncancerous cells have remained a high challenge. Diagnosis of ALL and lymphocyte subtypes are done by microscopic viewing examination of cells in the peripheral blood samples by hematologists. Since this exam is time-consuming, boring and dependent on the skill of the hematologists, automatic systems are desired to overcome these limitations. In this study, 312 microscopic images including 958 cells are obtained from blood samples of 7 normal subjects and 14 patients. The first step of proposed system is image enhancement to decreases the effects of various luminosity situations with transformation from RGB to HSV color space and then applying histogram equalization on V channel for equalizing the grey level of image lightness. Nuclei segmentation from the blood cell images is the second step and performed using fuzzy c-means (FCM) clustering. After identify cluster of nuclei, we performed opening and closing process in morphological operation binary in order to remove extra noises and fill some minor holes in the nuclei. Moreover, to discrete the link between nuclei, watershed transform was applied. Then, a set of quantitative features (five geometric features about the size and figure of a cell and 36 statistical features about the spatial arrangement of intensities of nuclei image) are extracted to characterize the properties of these nuclei. In the next step, due to high number of features, the best features are selected by exhaustive search of all of the subsets of features and 13 features are selected. The final step is the classification of L1, L2, L3, normal, reactive and atypical cells by applying Random Forest (RF) classifier and result in 98% accuracy. We compared RF classifier with two other commonly classifiers named: MultiLayer Perceptron (MLP), and multi-SVM classifier with more success especially for recognition of L1, normal and reactive cells. So, this system can be used as an assistant diagnostic tool for hematologists to recognize subtypes of ALL and lymphocyte.