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Tumor Recognition in Wireless Capsule Endoscopy Images


Suvidha Sawant, M.S. Deshpande


Vol. 15  No. 4  pp. 85-89


This paper implementation of simple algorithm for detection of tumor in endoscopy images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and different treatment. The wireless capsule endoscopy (WCE) image is visually examined by the physician for detection and diagnosis of gastrointestinal (GI) tract tumor. However this method of detection resists the accurate determination of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of endoscopy tumor based on the combination of algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the gastrointestinal tract image. The wireless capsule endoscopy images, are extracted by gray level co-occurrence matrix (GLCM) & principal component analysis (PCA) and wavelet transform (DWT) which are characterize multi resolution property of images. After performing, the probabilistic neural network (PNN) based feature selection classify the type of tumor.


Feature selection, Probabilistic Neural Network (PNN), texture, tumor recognition, wireless capsule endoscopy (WCE) image.