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Bridging the gap between mathematical and biological models and clinical applications could be considered as one of the new challenges of medical image analysis over the ten last years. This paper presents an advanced and convivial algorithm for brain glioblastomas tumor growth modelization. The brain glioblastomas tumor region would be extracted using a fast distribution matching developed algorithm based on global pixel wise information. A new model to simulate the tumor growth based on two major elements: cellular automata and fast marching method (CFMM) has been developed and used to estimate the brain tumor evolution during the time. On the basis of this model, experiments were carried out on twenty pathological MRI selected cases that were carefully discussed with the clinical part. The obtained simulated results were validated with ground truth references (real tumor growth measure) using dice metric parameter. As carefully discussed with the clinical partner, experimental results showed that our proposed algorithm for brain glioblastomas tumor growth model proved a good agreement. Our main purpose behind this research was of course to make advances and progress during clinical explorations helping therefore radiologists in their diagnosis. Clinical decisions and guidelines would be hence so more focused with such an advanced tool that could help clinicians and ensuring more accuracy and objectivity.

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Alzheimer's disease is a neurodegenerative disease that primarily affects the nervous system and is characterized by a progressive loss of nerve cells. In order to understand and explore such pathology, confocal microscope images could be widely used because of their interesting high clarity in term of cell identification. In fact, confocal microscopy is a potential tool that enables real-time imaging of living tissue at high resolution and high contrast. However, in practical microscopy, aberration-free confocal imaging is rarely achieved. So, aberration corrected images of biological specimens show considerable improvement. Geometric aberrations are considered as the main sources of image degradation. The purpose of this project was to develop an advanced method that is able to estimate the distortion parameters and to test these parameters to correct image deformations. Such method was based on the adaptation of the Zhang's algorithm implemented in the OpenCV library in order to calibration of confocal microscopy images and results of simulation were interesting and promoting and could involve other research areas such as stitching confocal microscopic images and working in high resolution.

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