Authors

• Stefan Giebel
• Jang Schiltz
• Norbert Graf
• Nasenien Nourkami
• Ivo Leuschner
• Jens-Peter Schenk

Abstract

The image recognotion and the classification of objects according to the images are more in focus of interests, especially in medicine. A mathematical procedure allows us, not only to evaluate the amount of data per se, but also ensures that each image is pro- cessed similarly. Here in this study, we propose the power of shape analysis, in conjunction with neural networks for reducing white noise instead of searching an optimal metric, to support the user in his eval- uation of MRI of renal tumors. Therapy of renal tumors in childhood bases on therapy optimizing SIOP(Society of Pediatric Oncology and Hematology)-study protocols in Europe. The most frequent tumor is the nephroblastoma. Other tumor entities in the retroperitoneum are clear cell sarcoma, renal cell carcinoma and extrarenal tumors, espe- cially neuroblastoma. Radiological diagnosis is produced with the helpof cross sectional imaging methods (computertomography CT or Mag- netic Resonance Images MRI). Our research is the first mathematical approach on MRI of retroperitoneal tumors (n=108). We use MRI in 3 planes and evaluate their potential to differentiate other types of tumor by Statistical Shape Analysis. Statistical shape Analysis is a methology for analyzing shapes in the presence of randomness. It allows to study two- or more dimensional objects, summarized according to key points called landmarks, with a possible correction of size and position of the object. To get the shape of an object without information about posi- tion and size, centralisation and standardisation procedures are used in some metric space. This approach provides an objective methodology for classification whereas even today in many applications the decision for classifying according to the appearance seems at most intuitive.

We determine the key points or three dimensional landmarks of retroperitoneal tumors in childhood by using the edges of the platonic body (C60) and test the difference between the groups (nephroblastoma versus non-nephroblastoma).

Keywords

• Neural Networks
• Radiology
• shape analysis