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A Texture Analysis Method Based on Statistical Contourlet Coefficient Applied to the Classification of Pancreatic Cancer and Normal Pancreas

Published: 29 December 2018 Publication History

Abstract

Purpose: To explore the value of a texture analysis method based on statistical contourlet coefficient in the computer-aided diagnosis of pancreatic cancer and normal pancreas. Methods: This paper proposed a texture analysis method based on statistical contourlet coefficient (SCC) to extract the quantitative features of regions of interest (ROIs) in non-enhanced CT images. The SCC method consisted of two steps. First, it decompose an ROI into several subbands at multiple directions in multiple layers, where a "9-7" filter was applied in the Laplacian pyramid filtering stage and a "pkva" filter was applied in the directional filtering stage. Then, it performed normalization on the coefficient matrices of the subbands and extracted the first and second order statistical features of the normalized matrices. Six traditional texture analysis methods that are widely used for medical image processing were used for comparisons. After the feature extraction, feature selection and classification (10-fold cross training and test) were performed, and the classification results were evaluated. Results: The proposed method achieved the best classification result: the average accuracy was 79.52%; the average sensitivity was 78.5%; the average specificity was 80.63%; the average AUC was 0.848. Conclusions: It indicates that the texture analysis method based on statistical contourlet coefficient is rewarding for computer-aided diagnosis of pancreatic cancer and normal pancreas using non-enhanced CT images. It can reduce the workload of radiologists and play a significant guiding effect on junior radiologists.

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  1. A Texture Analysis Method Based on Statistical Contourlet Coefficient Applied to the Classification of Pancreatic Cancer and Normal Pancreas

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    ISBDAI '18: Proceedings of the International Symposium on Big Data and Artificial Intelligence
    December 2018
    365 pages
    ISBN:9781450365703
    DOI:10.1145/3305275
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 29 December 2018

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    Author Tags

    1. normal pancreas
    2. pancreatic cancer
    3. statistical contourlet coefficient
    4. texture analysis

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    ISBDAI '18 Paper Acceptance Rate 70 of 340 submissions, 21%;
    Overall Acceptance Rate 70 of 340 submissions, 21%

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    • (2021)A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary LesionsInterdisciplinary Sciences: Computational Life Sciences10.1007/s12539-020-00410-7Online publication date: 7-Jan-2021

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