Abstract

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Title:: Image classification of esophageal cancer and hepatic hydatid disease based on shape and texture features

Author(s):: NADIYA·Abdukeyim1; YAO Juan2; LIU Zhihua3; YAN Chuanbo4; 1. Basic Medical College, Xinjiang Medical University, Urumqi 830011, China; 2. The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China; 3. College of Public Health, Xinjiang Medical University, Urumqi 830011, China; 4. Medical Engineering Technology College, Xinjiang Medical University, Urumqi 830011, China

Keywords:: Keywords: esophageal cancer; hepatic hydatid disease; medical imaging; feature extraction; K nearest neighbor

Abstract:: Abstract: Objective To explore the classification of Hu moment invariant features and wavelet transform texture features of the X-ray image of esophageal cancer and the CT image of hepatic hydatid disease by K nearest neighbor (KNN) classification algorithm. Methods Hu moment invariant and wavelet transform algorithms were used to extract the features of the X-ray image of esophageal cancer and the CT image of hepatic hydatid disease. Moreover, KNN classifier was used to classify the feature values for verifying the classification ability of the extracted features. Results For the X-ray image of esophageal cancer, Hu moment invariant algorithm had good classification performance in extracting shape features. Using wavelet transform algorithm to extract texture features of the CT image of hepatic hydatid disease also had preferable classification performance. Conclusion Hu moment invariant features combined with KNN classifiers provide a basis for the classification of esophageal cancer in Xinjiang Kazakh; and wavelet transform texture features combined with KNN classifiers provide a basis for the classification of endemic hepatic hydatid disease. The study also lays the foundation for the development of computer-aided diagnosis system.