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Breast tumor is one of the causes of women's death in the world after cardiovascular diseases. Recently, the diagnosis and treatment planning of this kind of tumors are based on magnetic resonance imaging techniques, which are the reference imaging modality in breast tumors analysis since it can better differentiate soft tissues (compared to mammography and ultrasound). Segmentation of the breast cancer is a very important task for cancer response prediction in neoadjuvant chemotherapy treatment based either on texture analysis or parametric response maps. In most of the previous works in the literature, the segmentation is generally done with manual annotation of tumor regions, which is time-consuming and error-prone. In this paper, we propose a new strategy for an automatic segmentation of breast tumors in MRI images. We propose first to separate the two breasts, and then, we use the Expectation–Maximization Algorithm to segment and detect the tumor lesion. [ABSTRACT FROM AUTHOR]

Image segmentation in brain magnetic resonance imaging (MRI) largely relates to dividing brain tissue into components like white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Using the segmentation outputs, medical images can be 3D reconstructed and visualized efficiently. It is common for MRI pictures to have issues such as partial volume effects, asymmetrical grayscale, and noise. As a result, high accuracy in brain MRI picture segmentation is challenging to achieve in practical applications. In this paper, we developed an effective algorithm for brain MRI image segmentation utilizing a combination of statistical and partial differential equation-based approaches, based on a neuro-mechanical model. The findings of this work demonstrate that by combining various segmentation approaches, it is possible to quickly segment brain MRI data at a degree of precision necessary for different applications. Here, we show that when we use nonlinear filtering, k -means clustering, and active contour modeling, we can get very good results when we segment brain MRI images. It is clear that the proposed approach has higher segmentation performance and can properly separate brain tissue from a large number of MRI images. [ABSTRACT FROM AUTHOR]