In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions; then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.

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Before the routine anesthesia, an airway examination must be performed during the pre-anesthetic examination for all patients who need a surgical operation in order to decide whether the tracheal intubation is easy or hard. In the field of anesthesia and intensive care, many works have been performed in order to reduce as much as possible the anesthetic risks and the mortality rate as well as to provide assistance to the Doctors Specialized in Anesthesia (DSA's).In this work, we suggest a system of Computer Aided Diagnosis for the DSA's during the anesthetic examination. This system was conceived to determine whether the patient's tracheal intubation is easy or hard. For this we tested four different classifiers which are the Multi-Layer Perceptron (MLP), the C4.5 decision tree, the Support Vector Machine (SVM) and the K-Nearest Neighbors algorithm (KNN) applied on a new database which was collected locally.We obtained promising results which are a proof of the reliability and the coherence of our database.

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