A new morphological segmentation algorithm for biomedical imaging applications
Dimitris S. Gorpas, Petros Maragos, Dido M. Yova · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Images of high geometrical complexity are found in various applications in the fields of image processing and computer vision. Medical imaging is such an application, where the processing of digitized images reveals vital information for the therapeutic or diagnostic algorithms. However, the segmentation of these images has been proved to be one of the most challenging topics in modern computer vision algorithms. The light interaction with tissues and the geometrical complexity with the tangent objects are among the most common reasons that many segmentation techniques nowadays are strictly related to specific applications and image acquisition protocols. In this paper a sophisticated segmentation algorithm is introduced that succeeds into overcoming the application dependent accuracy levels. This algorithm is based on morphological sequential filtering, combined with a watershed transformation. The results on various biomedical test images present increased accuracy, which is independent of the image acquisition protocol. This method can provide researchers with a valuable tool, which makes the classification or the follow-up faster, more accurate and objective.