Neural Foraminal Stenosis Classifications using Multi-Feature Hierarchical Clustering and Delineation

In recent years many people age above 25 are suffering from Neural Foraminal Stenosis (NFS) disease. Neural Formanial Stenosis is a problem of narrowing the spaces between the bones of the spine. It is curable at the earliest stage. A simple exercise can give relief to this problem. But Identification of this problem at the initial stage is a difficult task. It requires more screening techniques and knowledge about the problem. The motivation for this work is to identify the disease at the earliest stage. In clinically, it is identified by analyzing the CT and MRI images. Accurate boundary delineation is important in the clinical diagnosis and also for the proper treatment. Surveys say that boundary detection is subjective to the experience of the clinician. These variations make an impact on the detection of Boundary delineation. Image segmentation algorithms can be used to assist clinicians to detect the boundaries. The classifier can also be used to classify the level of the NFS. This paper utilizes the hierarchical clustering to detect the boundary of the affected region and it is found that, it gives a better assistance to the clinicians.