Detection of spleen from abdominal MRI images using neural networks and watershed conversion

Subject Areas : Electronics Engineering

Zohreh Miri ¹ , Alireza Zolghadr asli ² , Mehran Yazdi ³

1 - Islamic Azad University, Fasa Branch, M.Sc. Department of Electrical Electronics Engineering
2 - Faculty member of Shiraz University
3 - Faculty member of Shiraz University

Received: 2021-05-21 Accepted : 2021-05-21 Published : 2017-12-22

Keywords:

Abstract :

MRI is one of the most useful imaging techniques today. Abdominal MRI imaging is widely used in medical diagnoses such as tumors, tissue diagnostics, etc. Therefore, fast and appropriate segmentation algorithms play an important role in diagnosing diseases, classifying and quantifying tissue, isolating different elements and diagnosing tumors. In this paper, an automatic spleen separation system from abdominal MRI images is presented, which includes two stages of preprocessing and spleen separation algorithm. Pre-processing is used to de-noise and improve image quality. Isolation of the spleen consists of three stages of segmentation using watershed conversion, calculation of features, and the final step of comparing these features with reference values. Any element whose properties are closer to the reference properties is labeled as a spleen. A forward neural network was used to obtain the reference values, which are the same as the shape of the spleen.  The results of the spleen output obtained are compared with the spleen output extracted by a specialist, and the percentage difference between the two outputs is considered as an error.