Abstract
Three-dimensional image construction and reconstruction play an important role in various applications of the real world in the field of computer vision. In the last three decades, researchers are continually working in this area because construction and reconstruction is an important approach in medical imaging. Reconstruction of the 3D image allows us to find the lesion information of the patients which could offer a new and accurate approach for the diagnosis of the disease and it adds a clinical value. Considering this, we proposed novel approaches for the construction and reconstruction of the image. First, the novel construction algorithm is used to extract the features from an image using syntactic pattern recognition. The proposed algorithm is able to extract in-depth features in all possible directions and planes and also able to represent the 3D image into a textual form. These features vector is nothing but a string that consists of direction and length information in syntactic form. For the identification of syntactic grammar, a real 3D clay model was made and identified the different possible patterns in the image. According to the domain knowledge, in a 3D image, a pixel could be present in 26 possible directions and we incorporated all possible directions in the proposed algorithm. In the same way, for the reconstruction of the image novel algorithm is proposed. In this algorithm, the knowledge vector has been taken as an input and the algorithm is able to reconstruct a 3D image. Reconstruction allows us to explore the internal details of the 3D images such as the size, shape, and structure of the object which could take us one step ahead in the field of medical image processing. Performances of the proposed algorithms are evaluated on five medical image dataset and the datasets are collected from Pentagram research institute, Hyderabad and results are outperformed in real-time. The accuracy of the proposed method is 94.78% and the average execution time is 6.76 seconds which is better than state of art methods.
Incorporating Domain Knowledge into Medical Image Mining