Machine Learning to Perform Segmentation and 3D Projection of Abnormal Tissues by Endoscopy Images
Images obtained by endoscopy technique provides the normal direction of the tissue contour. This provides the important anatomical parameters which can be used for segmentation algorithms. Due to the variation of tissue image sizes, the values of intensity for the tissues is typically ununiformed and also have noisiness by nature. So, identifying the direction in normal by a single iteration is unreliable. A multi (factor)-iteration algorithm has been developed for estimating the direction normal to the edge of defective tissue. From experimented results, the estimation reliability is formulated by multiple iterations. The estimation post last iteration corrects the direction normally. We have obtained the balance at all points during the normal direction estimation and it is used by the Edge Detector. The implementation results obtained prove that our proposed algorithm reduces the amount of astonishing boundaries and gapes in the actual outlines. Thus improves the quality of segmentation and 3D projection. The obtained corrected output could also be used in the removal of false edges in post processing. The performance outcome of our proposed algorithm is measured at multiple iterations and results are tabulated.