Feature extraction for the analysis of colon status from the endoscopic images

BACKGROUND: Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS: Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN). RESULTS: Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (τ = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, τ = 1%) or color (90.52%, τ = 1%) features. CONCLUSION: In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method

A Novel Methodology for Extracting Colon's Lumen from Colonoscopic Images

Recently, computer assisted diagnosis on colonoscopic images is getting more and more attention by many researchers in the world, while the colon's lumen is the most important feature during the process. In this paper, a novel methodology for extracting colon's lumen from colonoscopic image is presented. At first, in order to eliminate the background at the outside of colonoscopic images, an effective and easy method, which is similar to the Hough transform is used to detect the preliminary region of interest (pROI). Then the original image is segmented through two steps: relaxation process and tightening process. The relaxation process is realized by finding the all valleys from the histogram of a defined homogeneity function to produce as many homogenous regions as possible, while tightening process is subsequently employed to merge the unnecessary regions according to the color difference between them in CIE (L* a* b*) color space. After a series of postprocessing procedure, the lumen is successfully extracted. An extensive set of endoscopic images is tested to demonstrate the effectiveness of the proposed approach