Journal of Medical Systems

, 43:29 | Cite as

An Enhancement of Computer Aided Approach for Colon Cancer Detection in WCE Images Using ROI Based Color Histogram and SVM2

  • P. Shanmuga SundaramEmail author
  • N. Santhiyakumari
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


The colon cancer is formed by uncontrollable growth of abnormal cells in large intestine or colon that can affect both men and women and it is third cancer disease in the world. At present, Wireless Capsule Endoscopy (WCE) screening method is utilized to identify colon cancer tumor at early stage to save the patient life who affected by the colon cancer. In this CTC method, the radiologist needs to analyze the colon polyps in digital image using computer aided approach with accurate automatic tumor classification to detect the cancer tumor at early stage. This kind of computer aided approach can operate as an intermediate between input digital image and radiologist. Therefore, in this paper, a novel computer aided approach is presented with ROI based color histogram and SVM2 to find the cancer tumor in WCE image. In this method, the digital WCE image can be preprocessed using filtering and ROI based color histogram depending on the salient region in colon. In common, the salient region can be distinctive because of low redundancy. Hence, the saliency is estimated by ROI based color histogram on the basis of color and structure contrast in given colon image for the further process of clustering and tumor classification in WCE image. The K-means clustering can be employed to cluster the preprocessed digital image to discover the tumor of colon. Subsequently, the features are extracted from the image in terms of contrast, correlation, energy and homogeneity by applying SGLDM method. The SVM2 classifier as input to classify the tumor is normal or malignancy using selected feature vectors. Here, the extracted features can also being combined to enhance the hybrid feature vector for the accurate tumor classification. Experimental results of proposed method can show that this presented technique can executes can tumor detection in colon image accurately reaching almost 95% in evaluation with existing algorithms.


Colon cancer Computer aided approach ROI extraction Image clustering Feature extraction SVM2 classifier 


Compliance with Ethical Standards

Conflict of Interest

The author’s has no conflict of interest in submitting the manuscript to this journal.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringKnowledge Institute of TechnologySalemIndia

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