Abstract
Feature extraction of medical images is a challenging task due to variation in modalities and imaged objects (organs, tissues and specific pathologies). This article presents a simple feature descriptor, local diagonal Laplacian pattern (LDLP), devised on the idea of local diagonal extrema pattern (LDEP). LDEP is implemented using first-order diagonal derivatives, whereas LDLP uses Laplacian operation. In order to reduce computational complexity, the intensities at each diagonal element are correlated to the rest of the three diagonal elements together with the centre pixel. Furthermore, the corner elements are compared with the centre pixel to improve the quality of feature description. In addition, the dimension of the pattern is lowered by a half. LDLP is applied to various MR, CT images of the TCIA database and CT images of the NEMA database that results in better feature description at less computational cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dhawan, A.P.: Introduction. In: Medical Image Analysis. pp. 1–22, John Wiley and Sons Inc., New Jersy (2011)
Nixon, M.S., Aguado, A.S.: Low-level feature extraction (including edge detection). In: Feature Extraction and Image Processing for Computer Vision, 3rd edn., pp. 137–216. Academic Press, London (2012)
Krig, S.: Interest point detector and feature descriptor survey. In: Computer Vision Metrics: Survey, Taxonomy, and Analysis, pp. 217–282. Apress (2014)
Ojala, T., Pietik\(\ddot{\text{a}}\)inen, M., Maenpaa T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 971–987 (2002)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision using Local Binary Patterns, pp. 13–47, Springer, London (2011)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Hikkilä, M., Pietikänen, M., Scmid, C.: Description of interest regions with center-symmetric local binary patterns. In: 5th Indian Conference, ICVGIP 2006, pp. 58–69. Springer, Berlin, Heidelberg (2006)
Ren, J., Jiang, X., Yuan, J., Wang, G.: Optimizing LBP Structure for visual recognition using binary quadratic programming. IEEE Signal Process. Lett. 21(11), 1346–1350 (2014)
Wang, K., Bichot, C.E., Zhu, C., Li, B.: Pixel to patch sampling structure and local neighboring intensity relationship patterns for texture classification. IEEE Signal Process. Lett. 20(9), 853–856 (2013)
Murala, S., Wu, Q.M.J.: Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J. Biomed. Health Inform. 18(3), 929–938 (2014)
Murala, S., Wu, Q.M.J.: Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval. Neurocomputing 119, 399–412 (2013)
Qi, X., Shen, L., Zhao, G., Li, Q., Pietik\(\ddot{a}\)nen, M.: Globally rotation invariant multi-scale co-occurrence local binary. Pattern Image Vis. Comput. 43, 16–26 (2015)
Murala, S., Maheswari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21(5), 2874–2886 (2012)
Kaya, Y., Ertuğrul, Ö.F., Tekin, R.: Two novel local binary pattern descriptors for texture analysis. Appl. Soft Comput. 34, 728–735 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases. IEEE Trans. Image Process. 24(12), 5892–5903 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J. Biomed. Health Inform. 99 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Process. Lett. 22(9), 1215–1219 (2015)
Gupta, R., Patil, H., Mittal, A.: Robust order-based methods for feature description. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 334–341. San Francisco (2010)
Haralick, R.M., Shapiro, L.G.: Conditioning and labeling. In: Computer and Robot Vision, pp. 303–357, Addison-Wesley Longman Publishing Co. Inc., Boston (1992)
DICOM Library. http://www.dicomlibrary.com/
The Cancer Imaging Archive (TCIA) Public Access. https://wiki.cancerimagingarchive.net/display/Public/TCGA-PRAD
Digital Imaging and Communications in Medicine (NEMA) Database. ftp://medical.nema.org/medical/Dicom/Multiframe/
The Cancer Genome Atlas, National Cancer Institute, National Human Genome Research Institute. https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp
The Cancer Imaging Archive (TCIA) Public Access. https://wiki.cancerimagingarchive.net/display/Public/RIDER+NEURO+MRI
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yelampalli, P.K.R., Nayak, J. (2018). Local Diagonal Laplacian Pattern A New MR and CT Image Feature Descriptor. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-10-6872-0_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-6872-0_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6871-3
Online ISBN: 978-981-10-6872-0
eBook Packages: EngineeringEngineering (R0)