Integration of Statistical Based Texture and Color Feature for Medical Image Retrieval
A.Saravanan1, S.Sathiamoorthy2
1A.Saravanan, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
2S.Sathiamoorthy, Tamil Virtual Academy, Chennai, India.
Manuscript received on 16 August 2019. | Revised Manuscript received on 23 August 2019. | Manuscript published on 30 September 2019. | PP: 5584-5588 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5567098319/2019©BEIESP | DOI: 10.35940/ijrte.C5567.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval.
Keywords: Autocorrelation Based Chordiogram Image Descriptor, Color Autocorrelogram, Correlation, Manhattan Measure.
Scope of the Article: System Integration