This research concerned a clinical need for precise breast cancer lesion characterization imaged by ultrasound sequences. Using therefore BI-RADS features that would be carefully extracted, the purpose of this study could be mainly to prove and to demonstrate the possibility of surveying precisely the changing characteristics of a breast cancer lesion within a considered ultrasound images' sequence. This was in fact a clinical need of a computer aided diagnosis (CAD) system permitting flexible and convivial clinical analysis of multi-slices' ultrasound breast cancer lesion with greater precision. The obtained results of our images' sequence breast cancer ultrasound analysis had shown the lesion form changing depending on the treated slice, as well as the values' differences for the morphological and the textural features. This would allow extracting more information about breast cancer lesions helping then radiologist to converge more rapidly and with a certain reinforced precision to the accurate clinical action to conduct. Such results would be reassembled and rearranged for constituting one computer aided diagnosis (CAD) system that could be provided for clinical explorations permitting on the other hand to avoid possible confusion between benign and malignant masses.

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Multiple sclerosis (MS) is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of multiple sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones segmentation performed using a new Gaussian mixture model based on various databases atlases. Afterward, lesion segmentation begins with the estimation of a lesion map, which is then subjected to threshold constraints and refined by a new lesion expansion algorithm. The evaluation was carried out on four clinical databases integrating various clinical cases which had different lesion loads and were presented by a set of MRI modalities at several noise levels. The results compared with those of the existing methods proved excellent cerebral segmentation with dice averages close to 0.8 and sensitivity and specificity averages greater than 0.9. In addition, depending on the used database, the lesion segmentation recorded mean values were close to or greater than 0.8 for the different metrics. The detection error and outline error averages were about 0.3. Besides the ability to identify the lesions affecting the different parts of the brain, even those spreading in the gray matter, the proposed methodology identified the lesions cores and their surrounding vasogenic edema. This has been thoroughly tested and validated by highly qualified radiologists and neurologists. The evaluation of the resulting discriminations recorded values close to or greater than 0.9 for dice, sensitivity, and specificity. As a valuable benefit, a computer aided diagnosis tool could be offered to clinicians. It would help efficiently during the MS diagnosis and avoid several confusions. Besides, it could be used for longitudinal survey and henceforth extends to other pathologies that could be explored by MRI modalities, such as glioblastoma or alzheimer's disease.