Reporting clinical stations as well as systems for environmental monitoring and disaster prevention currently making little use of the image processing algorithms, analytical and quantitative evaluations.
It is rather widely recognized by the scientific literature as the aforementioned application fields can benefit and be enriched with robust and reliable methods for pre-processing and improved image quality.
This method allows also the enrichment of the information content and selective / integrated visualization of volumetric data, multi-temporal, multi-parameter image segmentation and volumes and the use of statistics and quantitative spectral measurements for analysis and quality assessment. Therefore, the research aimed the development of mono (1D), two (2D) and three dimensional (3D) signals and video.
This method is completely automatic, since it does not require user intervention, and it is adaptable to the data analyzed. It is also independent from parameters and does not use any predefined template. Moreover, performing a data fusion, allows integrating multiple volumes, in order to obtain further information.
The method starts from a random point within the image and ends with the complete segmentation of the volume, automatically identifying intermediate points needed to complete segmentation. The result is the segmented volume, together with a list of significant seed points.
Due to the complete independence from the models and parameters, the method can also be used various application fields, different from the medical field.