Dept, of Surveying, Engineering Faculty, University of Isfahan, Isfahan, Iran- n_shahbaz2003@yahoo.com 
b Dept, of Surveying, Engineering Faculty, University of Isfahan, Isfahan and Dept, of Surveying, Engineering Faculty, 
University of Tehran, Tehran, Iran- Sattari@eng.ui.ac.ir 
Alzahra Educational Hospital, Medical Science University of Isfahan, Isfahan, Iran- mojtaba_ghazi_t@yahoo.com 
Working Group Sessions and related Poster Sessions, WgS - PS: WG V/6 
KEY WORDS: Image Processing, Detection, Computer vision, Feature Detection, Medicine, Feature Extraction, Feature 
Recognition 
ABSTRACT: 
In this paper we present and develop a set of algorithms, mostly based on morphological operators, for automatic colonic polyp 
detection applied to computed tomography (CT) scans. Initially noisy images are enhanced using Morphological Image Cleaning 
(MIC) algorithm. Then the colon wall is segmented using region growing followed by a morphological grassfire operation. In order 
to detect polyp candidates we present a new Automatic Morphological Polyp Detection (AMPD) algorithm. Candidate features are 
classified as polyps and non-polyps performing a novel Template Matching Algorithm (TMA) which is based on Euclidean distance 
searching. The whole technique achieved 100% sensitivity for detection of polyps larger than 10 mm and 81.82% sensitivity for 
polyps between 5 to 10 mm and expressed relatively low sensitivity (66.67%) for polyps smaller than 5 mm. The experimental data 
indicates that our polyp detection technique shows 71.73% sensitivity which has about 10 percent improvement after adding the 
noise reduction algorithm. 
INTRODUCTION 
Colon cancer death is among increasing causes of death (Jemal 
et al., 2004). Most colorectal cancer mortalities can be 
prevented by early detection and removal of colonic polyps 
(Robert Van Uiterta et al., 2006). A way to diagnose colonic 
polyps is to screen colon via colonoscopy. Figure 1 is a digital 
photograph from conventional colonoscopy showing a colonic 
polyp. 
Figure 1. Colonic polyp from conventional colonoscopy 
Although colonoscopy provides a precise means of colon 
examination, it is time-consuming, expensive to perform, and 
requires great care and skill by the examiner. Moreover, since 
colonoscopy is an invasive procedure, there is a fatal risk of 
injury to colon. In comparison with colonoscopy, Computed 
Tomography scanning is a technique for non-invasively 
performing colon cancer screenings. According to radiologists, 
it is not that simple to distinguish colon wall and successively 
colonic polyps from CT slices. Therefore, automatic polyp 
detection can make diagnostic processes reach a general level, 
not depending highly on the experts' special skills. In this 
regard, Vining et al., 1997 proposed a method to detect the 
colonic polyps by analysing the local curvature of the colon 
surface attaining 73% sensitivity. Summers et al., 2001 
developed a method that identifies the convex surfaces that 
protrude inward from the colon by evaluating the principle and 
mean curvature of the colon surface. Their method achieved 
29% to 100% sensitivity. Yoshida et al., 2002 proposed to use 
features such as the shape index (cup, rut, saddle, ridge, and 
cap) and curvedness values on small volume of interest and 
apply fuzzy clustering for polyp detection. They reported 89% 
sensitivity. Paik et al., 2000 proposed a technique based on 
contour normal intersection to detect surface patches along the 
colon wall and shows 85% to 90% sensitivity. Kiss et al., 2002 
combined the surface normal distribution and sphere fitting to 
produce 90% polyp sensitivity for polyps higher than 6mm. 
Kiss et al., 2003 employed the slope density function to 
discriminate between polyps and folds and their technique 
shows 85% sensitivity for polyps higher than 6mm. Paik et al., 
2004 developed a new technique based on surface normal 
overlap. Acar et al., 2001 suggested a method that detects 
spherical patches by Hough Transform and the algorithm 
analyses them using the optical flow to decide if they are polyps 
or not. Other interesting automated CAD techniques include the 
work of Gokturk et al., 2001, Acar et al., 2002, Wang et al., 
Corresponding author.