Abstract：
BACKGROUND:Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS:Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN). RESULTS:Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (tau = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, tau = 1%) or color (90.52%, tau = 1%) features. CONCLUSION:In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method.
journal_name
Biomed Eng Onlinejournal_title
Biomedical engineering onlineauthors
Tjoa MP,Krishnan SMdoi
10.1186/1475-925x-2-9keywords:
subject
Has Abstractpub_date
2003-04-08 00:00:00pages
9issn
1475-925Xjournal_volume
2pub_type
杂志文章abstract：BACKGROUND:Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aids. The aim of this study was to develop and evaluate automated classification of voice disorder in children with cochlear implantation and hearing aids. ME...
journal_title：Biomedical engineering online
pub_type： 杂志文章
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journal_title：Biomedical engineering online
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