Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

[1]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Hongping Cai,et al.  Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Florent Perronnin,et al.  Universal and Adapted Vocabularies for Generic Visual Categorization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Wu-Chung Shen,et al.  Computer-Aided Dianosis of Liver Tumors in Non-Enganced CT Images , 2004 .

[5]  Gang Hua,et al.  Discriminant Embedding for Local Image Descriptors , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  K. Kahn,et al.  Information content and clarity of radiologists' reports for chest radiography. , 1996, Academic radiology.

[7]  Arnaldo de Albuquerque Araújo,et al.  MammoSys: A content-based image retrieval system using breast density patterns , 2010, Comput. Methods Programs Biomed..

[8]  Haralampos Karanikas,et al.  A Pattern Similarity Scheme for Medical Image Retrieval , 2009, IEEE Transactions on Information Technology in Biomedicine.

[9]  Geoffrey McLennan,et al.  The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. , 2007, Academic radiology.

[10]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[11]  Jiri Matas,et al.  Improving Descriptors for Fast Tree Matching by Optimal Linear Projection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Mingrui Wu,et al.  Gradient descent optimization of smoothed information retrieval metrics , 2010, Information Retrieval.

[13]  Stephen E. Robertson,et al.  SoftRank: optimizing non-smooth rank metrics , 2008, WSDM '08.

[14]  P. Robinson,et al.  Radiology's Achilles' heel: error and variation in the interpretation of the Röntgen image. , 1997, The British journal of radiology.

[15]  Kehong Yuan,et al.  Brain CT image database building for computer-aided diagnosis using content-based image retrieval , 2011, Inf. Process. Manag..

[16]  Bipin C. Desai,et al.  Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion , 2008, Comput. Medical Imaging Graph..

[17]  Xiangrong Zhou,et al.  CAD on Liver Using CT and MRI , 2007, MIMI.

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  Carla E. Brodley,et al.  Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  L Kreel,et al.  Radiological-pathological correlation of mass lesions in the liver. , 1991, Australasian radiology.

[21]  Hayit Greenspan,et al.  Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework , 2007, IEEE Transactions on Information Technology in Biomedicine.

[22]  Jurgen J Fütterer,et al.  Variability in the Description of Morphologic and Contrast Enhancement Characteristics of Breast Lesions onMagnetic Resonance Imaging , 2005, Investigative radiology.

[23]  Konstantina S. Nikita,et al.  Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers , 2007, Artif. Intell. Medicine.

[24]  Hung-Khoon Tan,et al.  Experimenting VIREO-374: Bag-of-Visual-Words and Visual-Based Ontology for Semantic Video Indexing and search , 2007, TRECVID.

[25]  S. Kuo,et al.  Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems , 2005, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[26]  Jieping Ye,et al.  Regularized discriminant analysis for high dimensional, low sample size data , 2006, KDD '06.

[27]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[28]  E-Liang Chen,et al.  An automatic diagnostic system for CT liver image classification , 1998, IEEE Transactions on Biomedical Engineering.

[29]  Wu-Chung Shen,et al.  Computer-Aided Diagnosis of Liver Tumors in Non-enhanced CT Images , 2004 .

[30]  Tao Qin,et al.  A general approximation framework for direct optimization of information retrieval measures , 2010, Information Retrieval.

[31]  C. D'Orsi,et al.  Accuracy of screening mammography interpretation by characteristics of radiologists. , 2004, Journal of the National Cancer Institute.

[32]  Miroslaw Bober,et al.  MPEG-7 visual shape descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[33]  H. Greenspan,et al.  Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results. , 2010, Radiology.

[34]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[35]  T.G. Zhuang,et al.  Liver CT-image retrieval based on Gabor texture , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  Cor J. Veenman,et al.  Comparing compact codebooks for visual categorization , 2010, Comput. Vis. Image Underst..

[37]  Hui Wei,et al.  Creating Efficient Visual Codebook Ensembles for Object Categorization , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[38]  Hong Chang,et al.  Kernel-based distance metric learning for content-based image retrieval , 2007, Image Vis. Comput..

[39]  Farhad Farzanegan.  Keep AFIP. , 2006, Journal of the American College of Radiology : JACR.

[40]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[41]  Jiawei Han,et al.  SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis , 2008, IEEE Transactions on Knowledge and Data Engineering.