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Man has used medicinal plants for a long time. Among these Thymus vulgaris whose therapeutic virtues have been proven by many works, especially in the field of the fight against infectious diseases. The objective of this work is to evaluate the effect of essential oils, obtained by extraction from a medicinal plant Thymus vulgaris on the growth of Helicobecter Pylori. Various pro-cesses for extracting T. vulgaris essential oil from the Tessala region of SBA (Western Algerian) have been highlighted. Quantitative analysis of T. vulgaris essential oil after its hydrodistillation extraction showed a good yield of 3.2%.The study of the antibacterial effect of T. vulgaris essential oil against the H. pylori taken from gastric biopsies (cases of gastritis, ulcer, malignant tumor) within the Gastroenterology Department at the Hospital University Centre of SBA, was highlighted by the aromatogram test, the MIC and MBC test and the test of monitoring bacterial growth in the absence and presence of essential oil. The aromatogram test of H. pylori in the presence of T. vulgaris essential oil allowed us to test several inhibitory concentrations (10-1 to 10-5). The results of the aromatogram obtained showed a good activity of T. vulgaris essential oil in comparison with H. pylori. In all the three pathological cases (gastric malignant tumours, gastritis and ulcer). The results of the MIC and MBC tests allowed us to determine the minimum inhibitory concen-tration and that of bactericidal, whose highest recorded inhibition values of T. vulgaris essential oil in relation to H. pylori in all the three above stated pathological cases are (62.5, 125) μ.ml-1respectively.

Magnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high-performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region-growing algorithm, which is based on quasi-Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images.K E Y W O R D S brain tumor, expectation maximization, multisegmentation, quasi-Monte Carlo, region growing

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