Research Of Computer-aided Diagnosis Of Digestive Endoscopic Image

Computer-aided diagnosis is developed in recent decades, which is an important branch of bio-medical engineering. With the development of digital image technology, computer-aided diagnosis of digestive endoscopic image becomes possible. It makes full use of appropriate digital image processing techniques to analyse medical image and gets qualitative and quantitative analysis of the tissue, which can facilitate the diagnosis and treatment of doctors. The purpose of this paper is to propose a new method of computer-aided diagnosis of digestive endoscopic image for the automatic analysis of digestive endoscopic image.This paper uses a hierarchical approach to color digestive endoscopic image segmentation using homogeneity. In the first stage, the regions are segmented using a peak-finding algorithm on a 2-D histogram of homogeneity and intensity values. In the second stage, histogram analysis of the color feature hue is performed to subdivide the segmented regions obtained from the first stage. The subdivisions of different segmented regions having similar CIE(L*a*b) color measure are merged.In this paper, both color-based and texture-based quantitative features of color digestive endoscopic image are extracted. Specifically, we extract texture-based features from texture spectra and color-based features from color histogram. Integrated color and texture features of the image will have a stronger robustness.Bayesian classifier is one of the most commonly used method of statistical pattern recognition. This paper uses it to the diagnosis of digestive endoscopy image for the first time and obtains good results.For polyp detection, this paper proposes a novel scheme based on ellipse fitting for polyp detection in digestive endoscopic image. Firstly, a color edge detection algorithm is used to get the binary image. Then, we adopt a randomized ellipse detection algorithm based on the least square approach to detect polyps in the binary image. Experiment has achieved very good results and the algorithm can overcome the shortcoming of Bayesian classifier's missing detection of polyps.Experiment results suggest the feasibility of the proposed method for the diagnosis of digestive endoscopic image, which provides a new analysis method for the automatic diagnosis of digestive endoscopic image.