A Review on Computer-Aided Modelling and Quantification of PET-CT Images for Accurate Segmentation to Bring Imagination to Life

Abstract

Image segmentation is a process of dividing image into smaller parts to identify the individual objects. Often, this process helps in the quantification of digital images related to disease complications for metabolic process. This work reports on the use of computer-aided modelling tools and rapid prototyping technology to document, preserve and reproduce in three dimensions, and historic machines and mechanisms are used for accurate medical diagnosis. Epidemiological and clinical trials have confirmed the greater incidence and prevalence of deaths due to the inability to acquire qualitative information from the acquisition of images in primary stage itself. Rapid prototyping gives a better understanding of clinical and physiologic mechanisms of various disorders and pain, lesion detection. In image segmentation process, thresholding method is suitable for defining optimal value for identification and detection of region of interest. The standard uptake value (SUV) is based on selecting threshold value to utilize a similarity metric between the grey level of image and data points obtained from the threshold values. This is based on the intensities or inhomogeneity of clustering framework. Affinity propagation is used for images as a matrix by measuring the square patches from similarity texture. A major challenge in computer vision is to extract this information directly from the images available to us, help users, and to see and feel as an actual part in order to bring a computer image to life. Actually, the framework is given by PET-CT images which is used to identify and detect malignant tissues in a human body with accurate measurements of SUVs. This process involves ROI identification, segmentation, rendering and SUV functional quantification for promising results. The results obtained from computer modelling are transformed into real substance by rapid prototyping technology to feel and provide accurate diagnosis to patient.