Parametric Analysis of CT-image-Preprocessing for Improved Performance of Post-Processing Operation

Advanced technological tools in medical image analysis for disease detection and diagnosis are progressively coming into the utility of doctors and academicians due to various methodological evolution. Since the last three decades, various studies have been performed to achieve the state of the art predictive ability through early warning disease detection systems. After going through existing research work, it has been found that there is a lack of credibility in CT (computed tomography) image disease detection algorithms, which can be overcome by applying certain image processing and statistical analysis techniques. This article is made to describe a disparate approach in order to attain eminence in terms of lung disease diagnosis and detection. There are a huge amount of databases available online, but most of them encounter the issues of image noise and quality deterioration that further becomes the cause of irregularity and erroneous outcomes. The notion of this paper is to delineate an approach to pre-process input images and measure the quality of the given technique in order to choose better image operations and improve their visual information before analyzing them through a meticulous algorithm. An amalgamation of appropriate filters and image enhancement operations are also utilized to make clear insights of abnormality present inside of lung parenchyma. Furthermore, This study shows that the application of a high pass filter in the spatial domain improves the input image quality that is clearly identified by performing statistical analysis of output parameters. It is also observed that the otsu filtered image is more suitable to prepare the image for an efficient segmentation procedure. At last, it has been discussed that the overall approach in the form of pre-processing and its parameter estimation would not only help to assure quality enhancement of input image but also assist to run disease detection precisely in order to obtain reliable outcomes.