Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

Abstract

Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM)

“…Ptucha et al [ 5 ] proposed a liver segmentation method based on the sparse dictionary. The Singular Value Decomposition (K-SVD) algorithm [ 6 – 10 ] is used to train the dictionary and classifier, and the liver boundaries are determined based on the matching degree of the image and the dictionary. Subsequently, the liver can be segmented with less computational complexity.…”