title: Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform.
abstract: Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.
authors.affiliation: Industrial Electronics Department, Minho University, Portugal.
authors.forename: Daniel J C
authors.initials: DJ
authors.lastname: Barbosa
authors.forename: Jaime
authors.initials: J
authors.lastname: Ramos
authors.forename: Carlos S
authors.initials: CS
authors.lastname: Lima
mesh_terms: D000465:Algorithms; D053704:Capsule Endoscopy / Q000379:methods*; D003113:Colonoscopy / Q000379:methods*; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording / Q000379:methods*