Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.
The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T<sub>1</sub>-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.
Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.