The swift imaging speed, improved spatial resolution and lower cost compared to the magnetic resonance imaging is the reason behind the increased popularity of the computed axial tomography. In case of the esophageal tumor. The computed axial tomography utilizes the X-ray images to deliver the comprehensive images of the esophagus and the tissues surrounding it. It enables the detection of tumors or structural changes even at the developing stage and also gives the clear picture of the other distant organs like, lungs or liver affected by the cancer in the developed stage. One of the primary challenging chore in segmenting the esophageal tumor is because of the continuous variations in the position and the texture, intensity and as well as shape causing complexities in developing an standard procedure that could be applied universally. In this paper a semi-automated scheme is utilized in the segmentation of the esophageal tumors observed form the X-ray images of the computed axial tomography. An active- contour based semi-automated segmentation along with the procedures of level set is followed in the paper to segment the affected areas from the images of the esophagus acquired form the computed axial tomography. The strategy put forward segregates the work carried out into four major phases. The first phase extracts the images using the seed points. Second phase removes the unnecessary portions in the images, the threshold values are set in the third phase and the post processing is carried out in the fourth phase. This concept was evaluated on the real life data set of tumors collected from the nearby cancer treatment hospitals. The efficiency of the proposed strategy was compared with the previously existing state of art methods on the basis of the dice similarity, mean, medium and the maximal surface distance, and the Jaccard similarity. The concept put forth minimize the time utilization and also allows to have enhanced visualization of the tumors in the esophagus.

Esophageal Tumor Semi Automated Segmentation Active Contour Computed Axial Tomography Enhance Visualization