A new method for finding clusters embedded in subspaces applied to medical tomography scan image

Abstract

In this paper a new subspaces clustering algorithm is proposed. This method has two levels, the first one is an iterative algorithm based on the minimization of an objective function. The density is introduced in this objective function where the distances between points become relatively uniform in high dimensional spaces. In such cases, the density of cluster may give better results. The idea of the second level is to find the clusters in each subspace individually. We applied the proposed method to medical tomography scan image without Intravenous or IV contrast dye. Then we compare the results with the same image with IV contrast. However in some cases, there are risks associated with this injection, where the mortality risk is low but not null. This method can reduce the use of this injection. Experimental results on synthetic and real datasets show that the proposed method gives good results in medical tomography image.

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

“…Using medical images, the technique was implemented. Boulemnadjel and Hachouf [34] have presented a technique of subspace clustering considering medical images. Paul et al [35] have presented a simplified clustering technique that assists in the detection of specific diseases.…”